Responsibilities
Build, train, and deploy machine learning and AI models across supervised, unsupervised, and deep learning workflows.
Integrate and fine-tune pre-trained models (LLMs, vision, NLP) from platforms such as OpenAI, Anthropic, AWS Bedrock, Azure AI, and Hugging Face.
Develop end-to-end ML pipelines covering data processing, model training, and inference.
Create APIs, web applications, and microservices to serve models in production environments.
Work closely with data and software engineering teams to deliver scalable, maintainable systems.
Optimize models for performance, latency, scalability, and cost.
Implement model evaluation, monitoring, and performance tracking.
Stay updated on emerging AI tools, frameworks, and techniques.
Basic qualifications
Demonstrated experience developing and deploying ML models (regression, classification, NLP, computer vision).
Strong proficiency in Python and major ML/DL frameworks such as PyTorch, TensorFlow, or Keras.
Hands-on experience with AI APIs and SDKs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, etc.).
Ability to build RESTful APIs, web apps, or microservices using FastAPI, Flask, or Django.
Familiarity with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
Experience with semantic databases (Neo4j, RDF stores, knowledge graphs) and relational databases (PostgreSQL, MySQL).
Solid understanding of version control, model evaluation, and deployment best practices.
Preferred qualifications
Experience with MLOps workflows (CI/CD, model registries, monitoring, governance).
Knowledge of data orchestration tools (Airflow, Prefect, Kafka).
Exposure to vector databases (FAISS, Pinecone, Weaviate) and RAG architectures.
Familiarity with prompt engineering or LLM fine-tuning.
Experience building AI-driven applications such as chatbots or recommendation engines.