Description

Lead the design, development, and production deployment of generative AI and machine learning applications, ensuring scalable MLOps practices.

This role is on-site.

Responsibilities

• Architect and implement RAG pipelines, prompt engineering strategies, and agentic frameworks using LLMs like Gemini, Claude, and Llama.
• Develop and tune ML models, integrating them into enterprise applications via APIs and orchestration tools.
• Deploy GenAI models to production using MLOps principles, ensuring robust evaluation and safety guardrails.
• Manage containerized applications on OpenShift or Kubernetes for scalable cloud-native deployment.
• Cross-functional collaboration to solve complex, ambiguous problems and deliver high-throughput data solutions.

Required Skills

• 10+ years of experience in Data Science, Machine Learning, and AI fundamentals.
• Strong proficiency in Python, including Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, and FastAPI.
• Hands-on experience with Generative AI libraries: LangChain, LlamaIndex, and Transformers.
• Expertise in vector databases (PG Vector, Pinecone, Mongo Atlas) and knowledge graphs (Neo4j).
• Proven track record of deploying models to production with strong MLOps knowledge.
• Experience with container orchestration platforms like Kubernetes or OpenShift.
• Working knowledge of LLMs (Google Gemini, OpenAI, Anthropic, Mistral, Llama) and open-source models.

Preferred Skills

• Deep expertise in advanced RAG techniques and prompt tuning.
• Experience with Natural Language Processing (NLP) and neural networks.

Key Skills

Education

Bachelor's degree