LATAM, LATAM, LATAM Crehana is a B2B SaaS platform powered by AI that delivers integrated talent management solutions to clients across Latin America — skills, learning, performance, and career development in one place. Our mission: make people development universal. We're looking for a hands-on Tech Lead. Someone who leads by example: you set the technical direction, mentor your team, and when the moment calls for it, you roll up your sleeves and write code. What you'll own: Architecture and technical decisions for scalable backend services Code quality standards, code reviews, and CI/CD practices Mentorship and growth of your engineering team Collaboration with Product and DevOps to ship impactful features Stability, security, and evolution of our platform Tech stack: Python + FastAPI (required) Docker / Kubernetes / microservices AWS, GCP, or Azure PostgreSQL, MySQL, MongoDB REST APIs / GraphQL (nice to have) Terraform (nice to have) Apply: https://www.crehana.com/p/crehana/jobs/2ca97417-9f01-48a6-a426-7950565e7672/ We're looking for a hands-on leader who still writes code when it matters. This could be someone who has already led engineering teams working with Python and modern backend stacks — or an experienced engineer with deep technical expertise who is ready to step into a leadership role for the first time. Must-have Experience leading engineering teams (junior and mid-level), or a seasoned engineer ready to take that next step Strong Python proficiency with FastAPI Hands-on experience with microservices, Docker, and Kubernetes CI/CD pipelines and automated testing practices SQL and NoSQL databases: PostgreSQL, MySQL, MongoDB Cloud platforms: AWS, GCP, or Azure Security awareness and compliance standards Strong communication, collaboration, and problem-solving skills Comfortable with agile methodologies (Scrum, Kanban) Nice to have RESTful API design and/or GraphQL Terraform for infrastructure as code

Bangalore, Pune, India We are seeking a highly skilled and innovative Python / AI Engineer to join our team and help us build the next generation of AI-powered enterprise systems. We are actively investing in LLMs, agentic workflows, and applied AI systems, but much of this potential is still untapped. Today, our AI initiatives are fragmented—PoCs exist in silos, LLM integrations are early-stage, and there is no unified intelligence layer across the application landscape. We’re looking for someone who can change that. You will sit at the intersection of engineering, R&D, and business teams, turning AI experimentation into production-grade systems that deliver measurable impact. This is not a “model training-only” role—you will be expected to design, build, and ship end-to-end AI solutions. The Role You will be the first deep technical owner responsible for turning our AI ambition into real, working systems that drive business outcomes. Build and productionize AI systems — Design and implement LLM-powered applications, AI agents, and intelligent automation workflows that integrate directly into our core product ecosystem. Develop PoCs and scale them into production — Rapidly prototype AI/ML solutions, validate feasibility with stakeholders, and evolve them into scalable, reliable services. Integrate LLMs into real business workflows — Work with APIs, orchestration frameworks, and model providers to embed LLM capabilities across applications (chat, reasoning, summarization, retrieval, and automation use cases). Design intelligent systems, not just models — Focus on end-to-end architecture: data flow, prompt orchestration, memory, evaluation, guardrails, and feedback loops. Bridge R&D and engineering — Translate research ideas and business problems into working AI systems that can be deployed, monitored, and improved iteratively. Drive AI adoption across the organization — Collaborate closely with product, engineering, and business stakeholders to identify high-impact opportunities where AI can meaningfully improve efficiency or decision-making. Skills required: 1.Minimum 8+ years of experience in AI/ML development. 2.Advanced proficiency in Python. 3.Strong understanding of AI frameworks (e.g., TensorFlow, PyTorch). 4.Expertise in at least one LLM (e.g., GPT, Claude, LLaMA). 5.Solid foundation in Data Science and statistical modeling. 6.Experience with Agentic Workflows using LangChain-based agents. 7.Hands-on experience with LangGraph (e.g., hackathons or MCPs). 8.Deep understanding of fine-tuning techniques for LLMs. 9.Hands-on experience with Transformer architecture and its evolution from earlier models. Responsibilities: 1.Design and develop PoCs and present them to business stakeholders. 2.Build and deploy innovative AI solutions using state-of-the-art models. 3.Integrate LLMs into existing and new applications. 4.Develop new functionalities within R&D applications. 5.Promote and implement best process practices across AI development workflows.

Illinois, Chicago, United States We are looking for an experienced Python / DevOps engineer to complete a one-time private setup of a Chatterbox-based voice-cloning system. The role includes deploying the model in a secure private environment, building a simple internal web interface for non-technical users, and ensuring that all voice samples and generated audio remain inside our own infrastructure. This should be a straightforward project for someone experienced with Python, server deployment, and basic web application setup. Job requirements Strong experience with Python and Python environments. Experience deploying AI or machine-learning models on servers. Basic web development skills to build a small internal web page or API. Understanding of security and access control for internal tools. Ability to work independently on a one-time setup project. Clear communication and ability to provide simple handover documentation. Experience with DevOps, server deployment, or internal tools is a plus.

REMOTE, REMOTE As a software engineer, you'll join a team tackling complex challenges in automating, streamlining, and scaling engineering and support workflows through the development of critical APIs and applications. This role requires actual engineering thinking to build robust internal systems. Ready to tackle complex engineering challenges that directly impact how our engineering and support teams work? Who we are NOT looking for: Agencies People who are looking for a side “hustle” or are starting their own business To apply: please fill out this form: https://forms.clickup.com/2257368/f/24wer-176357/RX6L92YGBHLB386KYI Additionally, please start your cover letter with: “I AM A PYTHON ENGINEER FOCUSED ON DEVOPS AND INTERNAL TOOLS” (in ALL CAPS) Required Skills: Core Engineering Strong Python experience Test-driven development (TDD) and pytest proficiency Familiarity with, and adherence to, PEP guidelines and Python coding standards Thoughtful development centered around performance, deeply optimized code, and proper code organization Bug identification and resolution with local testing Infrastructure & DevOps Experienced with Linux at the terminal level RESTful/CRUD API development and design best practices Designing and implementing resilient, internal-facing APIs and infrastructure tools Security practices and vulnerability prevention Git version control with branching strategies Databases PostgreSQL experience: schema design, optimization, advanced queries Professional Requirements English fluency (written/verbal) Previous experience working for a US-based company

100% Remote, USA Six Feet Up is seeking a Senior Data Engineer with deep experience designing, building, and maintaining secure, scalable data systems. In this role, you will work with cross-functional teams to turn complex data challenges into reliable production solutions. You will design data pipelines, build processing workflows, improve data quality, support machine learning initiatives, and help clients make better use of structured, unstructured, time-series, sensor-based, and high-volume data. We are looking for someone who can bring technical leadership, sound engineering judgment, and strong communication skills to ambiguous data problems. The ideal candidate is comfortable working across data engineering, cloud infrastructure, machine learning support, and production software delivery. As a Senior Data Engineer, you will: Design, build, and maintain robust, scalable data pipelines Develop ETL/ELT workflows for collecting, transforming, validating, and storing data Work with cloud-based data processing and storage systems Implement data validation, quality checks, monitoring, and transformation workflows Process complex datasets, including noisy, high-volume, time-series, sensor, or device-generated data Support machine learning workflows, including data preparation, model training, evaluation, and production integration Collaborate with data scientists, researchers, software engineers, and client stakeholders Translate prototype data workflows into reliable, maintainable production systems Create systems that are well-documented, testable, secure, and ready for audit or review Communicate technical tradeoffs clearly to both technical and non-technical audiences If you are a motivated Senior Data Engineer seeking a challenging and meaningful role, we encourage you to apply. We are looking for someone with strong hands-on experience in: Data pipeline architecture and implementation ETL/ELT orchestration tools such as Airflow, Dagster, or similar platforms Python-based data engineering and data processing tools Cloud-based data infrastructure, storage, and processing Data modeling, schema design, validation, and quality-control practices Working with public, proprietary, structured, and unstructured datasets Time-series, sensor, IoT, or other high-volume data sources Supporting machine learning or data science teams with reliable data workflows Building reproducible, testable, and maintainable data systems Version control, automated testing, and collaborative software development practices Strong candidates may also have experience with: Machine learning pipelines for classification, prediction, recommendation, or categorization systems MLOps, model evaluation, experiment tracking, and reproducible ML workflows Processing noisy signal data or other data that requires cleaning, filtering, or feature extraction Healthcare, digital health, research, or regulated software environments HIPAA, privacy-preserving data architecture, or secure cloud data processing Working with sensitive, clinical, or user-generated data Helping researchers or domain experts scale early-stage algorithms into production-ready systems Designing systems that support thousands or more users A Plus: Experience with containers, CI/CD, DevOps practices, or Kubernetes Familiarity with AWS, Google Cloud, Azure, or similar cloud platforms Experience with consumer analytics, dashboards, or data visualization products Experience supporting FDA, SaMD, clinical validation, audit, or regulatory documentation efforts Experience designing secure data workflows for privacy-sensitive applications About You You are a senior engineer who brings structure to ambiguous technical challenges. You ask thoughtful questions, identify risks early, and know how to balance research needs, business goals, engineering quality, and long-term maintainability. You care about data quality, privacy, testing, documentation, and clear communication. You are comfortable designing architecture, writing production code, reviewing data workflows, and collaborating with people from different technical backgrounds. You enjoy helping clients move from ideas and prototypes to secure, scalable, production-ready systems.

Durham, North Carolina, USA We are looking for Agentic AI Scientists eager to utilize their expertise in these advanced technologies to revolutionize our drug development processes. In the Pharmaceutical Technology and Development (PT&D) department, you will be a key player in transforming molecules into groundbreaking medical treatments. Your role involves contributing data science expertise into cross functional global pharmaceutical development projects in support of transforming the way we deliver medicines to patients. You'll play a pivotal role in shaping our AI strategy and driving the co-development of sophisticated HITL multi-agent systems. We are hiring two candidates for this position and the roles will be based at our dynamic site in Durham (USA). Essential skills/Experience: PhD in computer science, data science, artificial intelligence, machine learning or related fields. At least 3 years of experience in Deep Learning and ML Excellent coding skills in languages such as Python, R. Hands-on industrial experience designing multi-agent patterns, digital twins and experience with agentic AI design patterns, reinforcement learning. Extensive industrial experience with AI and ML frameworks like TensorFlow, PyTorch, Hands-on experience with GenAI orchestration frameworks such as LangGraph, CrewAI Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable Baselines. Hands-on industrial experience with applied machine learning domains such as deep learning, NLP, GenAI. Desirable skills/experience: Contributions to open-source projects. If you meet these criteria, please highlight merged GitHub PRs in your application. Strong publication record in the field of AI. Experience designing multi-agent systems in the pharmaceutical sector. Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry. Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.

Montreal, QC, Canada Role Overview As Data Acquisition & Infrastructure Engineer, you will be the foundation of MAI's data capabilities. Your primary focus will be the development and operation of automated data collection pipelines that aggregate publicly available information from across the art market ecosystem. You will also own the design and maintenance of the underlying database infrastructure — built on PostgreSQL and AWS — that stores and serves this data. This is a high-ownership role. The infrastructure is partially built; you will take it to production scale. You will work closely with AI engineers, a computer vision engineer, a product manager, full-stack developers, and the Head of Art Research, reporting directly to the Head of AI Engineering. Key Responsibilities DATA PIPELINE & COLLECTION Architect and maintain automated pipelines that collect, normalize, and ingest publicly available art market data from web-based sources Build reliable, maintainable collection systems using Python (Scrapy, BeautifulSoup, Playwright, or equivalent), with a strong emphasis on resilience, scheduling, and data freshness Manage pipeline orchestration and scheduling using tools such as Apache Airflow, AWS EventBridge, or cron Navigate the practical challenges of large-scale public data collection, including access patterns, rate constraints, and source reliability Handle messy, inconsistent real-world datasets — clean, transform, and standardize data for downstream consumption DATABASE ENGINEERING Design, build, and maintain relational database schemas in PostgreSQL (hosted on Amazon RDS) to support complex, multi-entity art market data — artists, works, transactions, provenance, and valuation history Develop and optimize queries, indexes, and data models to ensure performance at scale Establish and enforce data quality standards, validation rules, and integrity constraints across the database Collaborate with AI engineers and the computer vision team to ensure the data layer supports model training and inference requirements INFRASTRUCTURE & OPERATIONS Deploy and manage pipeline workloads on AWS (Lambda, EC2, S3, RDS) Monitor pipeline health, data freshness, and system reliability — proactively address failures Contribute to infrastructure-as-code practices as the team scales Core Requirements 3–5 years of professional experience in data engineering or a closely related discipline Proven experience building and maintaining automated data collection pipelines from web-based public sources using Python (Scrapy, BeautifulSoup, Playwright, or Selenium) Strong data cleaning and normalization skills, with demonstrated ability to handle heterogeneous, inconsistent real-world datasets Solid PostgreSQL experience: schema design, query optimization, and database maintenance Hands-on AWS experience: Lambda, EC2, S3, RDS Experience scheduling and orchestrating data pipelines (Apache Airflow, AWS EventBridge, or equivalent) Experience navigating the constraints of large-scale public data collection, including reliability, access patterns, and data freshness challenges Nice to Have Knowledge of data quality frameworks and validation pipeline design Experience with containerization (Docker) and infrastructure-as-code (Terraform, AWS CDK) Familiarity with ETL/ELT tooling (dbt, AWS Glue, or equivalent) Exposure to art market platforms (Christie's, Sotheby's, Artsy, Artnet) or understanding of how auction and gallery data is structured Background or genuine interest in the art world, collectibles, or alternative asset markets Experience in a startup or early-stage environment where ownership and adaptability are essential