Lead AI/ML Developer

Job Title:

Location:

Role Summary

We are seeking a seasoned Artificial Intelligence & Machine Learning (AI/ML) Tech Lead to drive the technical design, development, and deployment of AI solutionsincluding fine-tuning foundation models, building agentic applications, and implementing production-grade Retrieval-Augmented Generation (RAG) pipelines. This role requires close collaboration with pre-sales teams, account delivery managers, solution architects, and enterprise clients to define and deliver AI solutions tailored to business needs.

The ideal candidate will provide hands-on technical leadership throughout the AI/ML lifecycle—leading proof-of-concept (POC) efforts, conducting solution demos, and overseeing production-grade implementations. They will also mentor engineering teams, enforce best practices, and ensure the successful delivery of AI initiatives  

Key Responsibilities

1. Lead Technical Delivery & Mentorship

• Lead and mentor a team of 6–10 engineers; establish coding standards, conduct design and PR reviews, and drive continuous improvement.
• Foster a culture of knowledge-sharing through KT Sessions, documentation, and best-practice guides.

2. AI/ML Model Development & Optimization

• Develop, fine-tune, and optimize models using PyTorch, TensorFlow, and modern ML frameworks.
• Apply prompt engineering and advanced techniques to foundation models (e.g., GPT-4, Claude, Llama).
• Deliver NLP solutions such as document classification, sentiment analysis, summarization, entity extraction, conversational AI, and generative content/workflow automation.
• Design and implement RAG workflows leveraging vector databases, smart chunking, ranking, and caching for accurate, grounded responses.
• Build multi-agent systems for task decomposition, planning, and tool usage in complex environments.

3. MLOps & Productionization

• Implement MLOps best practices: CI/CD pipelines, model monitoring, feature stores, lineage, and governance.
• Ensure model reproducibility, drift detection, explainability (SHAP, LIME), and responsible AI practices.
• Optimize inference throughput/latency and ensure robust rollback strategies.

4. Performance, Security, and Compliance

• Ensure security, compliance, and performance of AI solutions, adhering to industry standards and regulations.
• Integrate with external APIs, optimize for cost/latency, and manage observability.

5. Stakeholder Engagement & Roadmapping

• Translate business objectives into technical designs; communicate risks, metrics, and impact to executives and stakeholders.
• Produce design diagrams, runbooks, and model cards; lead knowledge-sharing sessions and workshops.

Technology Stack

• Programming Languages & Frameworks
• Python (expert)
• JavaScript/Go/TypeScript (nice-to-have)
• Strong knowledge of libraries such as Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, TensorFlow, PyTorch.
• PyTorch, TensorFlow/Keras, Hugging Face Transformers/PEFT, LangChain/LlamaIndex, Ray/PyTorch Lightning, FastAPI/Flask
• Experience working with RESTful APIs, authentication (OAuth, API keys), and pagination
• Cloud & DevOps
• Expertise in one or more cloud vendors like AWS, GCP, Azure
• Containers (Docker), Orchestration (Kubernetes, EKS/GKE/AKS)
• Infrastructure as Code (nice-tohave) 
• MLOps
• Experiment Tracking: DVC, Weights & Biases, Neptune, TensorBoard etc
• Databases
• Relational: PostgreSQL, MySQL
• NoSQL: MongoDB / DynamoDB
• Vector Stores: FAISS / pgvector / Pinecone / OpenSearch / Milvus / Weaviate
• RAG Components
• Document loaders/parsers, text splitters (recursive/semantic), embeddings (OpenAI, Cohere, Vertex AI), hybrid/BM25 retrievers, rerankers (Cross-Encoder)
• Multi-Agent Frameworks
• Crew AI / AutoGen / LangGraph / MetaGPT / Haystack Agents, planning & tool-use patterns
• Testing & Quality
• Unit/integration testing (pytest), guardrails, hallucination tests, behavioral evals
• Security & Compliance

Best Practices & Design Governance

• Apply industry best practices: Secure AI development, responsible AI, model explainability, bias detection, and privacy-preserving techniques.
• Champion documentation standards, reusable reference architectures, and model cards.
• Participate in architecture review boards and provide guidance to ensure consistency and quality across AI initiatives.

Leadership & Pre-Sales Experience

• Proven track record shipping ML products at scale 
• Lead client workshops, technical discovery, and early-stage assessments.
• Support business development by identifying architectural differentiators and scalable patterns.

Qualifications

• 10–12 years of experience in software engineering/data science, with 4+ years leading AI/ML projects end-to-end.
• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Data Science, or related field.
• Certifications preferred: AWS Certified Machine Learning / Google Professional Machine Learning Engineer / Azure AI Engineer Associate and Kubernetes CKA/CKAD.
• Experience in regulated industries (Fintech, Healthcare, eCommerce) is a plus.

Soft Skills & Leadership Attributes

• Influential communication: Translate complex ML concepts for non-technical stakeholders; strong presentation & storytelling.
• Mentorship mindset: Coach, upskill, and inspire cross-functional teams; foster psychological safety.
• Ownership & bias for action: Deliver POCs, iterate based on feedback, drive solutions to production.
• Critical thinking & experimentation: Data-driven decision making, hypothesis testing, A/B experimentation.
• Adaptability: Stay current with rapid advances in GenAI, tooling, and research; evaluate emerging models/services.