Lead AI ML Engineer

We build breakthrough software products that power digital businesses. We are an innovative product development partner whose solutions drive rapid revenue, market share, and customer growth for industry leaders in Software and SaaS, Media and Publishing, Information Services, and Retail. Our key differentiator is our Product Mindset. Our development teams focus on building for outcomes and all of our team members around the globe are trained on the Product Mindset’s core values – Minimize Time to Value, Solve For Need, and Excel at Change. Our teams apply this mindset to build digital products that are customer-facing and revenue-generating. Our business-minded approach to agile development ensures that we align to client goals from the earliest conceptual stages through market launch and beyond. In 2024, 3Pillar Global India was named as a “Great Place to Work” for the fifth year in a row based on how our employees feel about our company, collaborative culture, and work/life balance - come join our growing team Minumum Qualification

8+ years of experience building and deploying AI/ML models (ML, NLP, deep learning).

Proven experience delivering ML solutions from concept to production.

Strong leadership and ability to communicate technical concepts to non-technical stakeholders.

Proficient in Python with experience using PyTorch, TensorFlow, Scikit-learn.

Hands-on experience with data preparation, feature engineering, model training, and tuning (Pandas, Spark).

Experience with NLP, computer vision, LLMs (APIs or fine-tuning), Agentic AI, and MCP-based architectures.

Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps/CI/CD tools (MLflow, GitHub Actions).

Knowledge of vector embeddings, vector stores, RAG, and LLM frameworks (LangChain, LlamaIndex).

Experience with model monitoring and performance tracking tools.

Additional Desired Experience

Led teams building GenAI, LLM, Agentic AI, or MCP-driven applications.

Experience with vector databases (FAISS, Pinecone) and Hugging Face ecosystem.

Built reusable AI frameworks, platforms, or internal tooling.

Contributions to open-source projects, research papers, or technical blogs.