Aziro - Technical Lead - Artificial Intelligence/Machine Learning

AI / ML Lead

Exp : 10+ yrs

Location : Any Aziro Location

Artificial Intelligence & Machine Learning Tech Lead

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 solutions including 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 610 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.

5. 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.

6. 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 to have)

- 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

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 to 12 years of experience in software engineering/data science, with 4+ years leading AI/ML projects end-to-end.

- Bachelors or Masters 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.