Lead AI Engineer

Responsibilities

• Develop and implement AI solutions leveraging fine-tuned Large Language Models (e.g., OpenAI models, LLaMA, Mistral).
• Design, develop, and optimize Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases (e.g., FAISS, Pinecone, Milvus).
• Build and enhance agentic AI systems utilizing frameworks like LangChain, AutoGPT, or similar automation frameworks.
• Deploy scalable ColBERTv2 architectures for semantic retrieval and classification.
• Create robust pre-processing and post-processing pipelines to enhance model performance, accuracy, and interpretability.
• Collaborate closely with cross-functional teams, including product managers, business stakeholders, data scientists, and software engineers.
• Implement best practices in model distillation, quantization, and optimization for deployment in production environments.
• Ensure compliance with enterprise-grade security, privacy standards, and data governance practices.
• Provide leadership and mentorship to team members, supporting their technical development and career growth through coaching, training, and performance feedback.
• Drive timely and successful completion of AI/ML projects by setting clear milestones, tracking progress, removing blockers, and aligning resources.

Requirements

• Bachelor’s degree in computer science, Data Science, Machine Learning, AI, or related fields; advanced degree strongly preferred.
• 5+ years of proven experience developing and deploying production-grade AI/ML systems.
• Strong programming skills in Python, familiarity with libraries/frameworks such as PyTorch, TensorFlow, Hugging Face, and LangChain.
• Demonstrated expertise with LLM fine-tuning (e.g., LoRA, PEFT), distillation, and model optimization.
• Practical experience implementing RAG pipelines with embedding technologies and vector stores (e.g., FAISS, Pinecone).
• Proven track record building agentic AI systems capable of interacting with multiple enterprise applications and platforms.
• Solid understanding of NLP techniques, Transformer architectures, semantic search, and document retrieval technologies (e.g., ColBERT).
• Hands-on experience with reinforcement learning techniques, including designing, training, and deploying reinforcement learning models.

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• Familiarity with cloud-based AI services (e.g., AWS SageMaker, Azure ML, Google Vertex AI).
• Experience with containerization (Docker, Kubernetes) and deployment pipelines (CI/CD).
• Knowledge of advanced AI frameworks and model inference engines such as Triton Inference Server, TensorRT, and ONNX.
• Familiarity with model monitoring, observability tools, and techniques to ensure long-term reliability and performance.
• Strong communication and interpersonal skills with the ability to clearly articulate complex technical solutions to non-technical stakeholders.
• Experience in regulated industries or environments requiring strict compliance and data governance standards.