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 & FrameworksPython (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 / FlaskExperience working with RESTful APIs, authentication (OAuth, API keys), and paginationCloud & DevOps :
Expertise in one or more cloud vendors like AWS, GCP, AzureContainers (Docker), Orchestration (Kubernetes, EKS / GKE / AKS)Infrastructure as Code (nice to have)MLOpsExperiment Tracking : DVC, Weights & Biases, Neptune, TensorBoard etcDatabasesRelational : PostgreSQL, MySQLNoSQL : MongoDB / DynamoDBVector Stores : FAISS / pgvector / Pinecone / OpenSearch / Milvus / WeaviateRAG ComponentsDocument loaders / parsers, text splitters (recursive / semantic), embeddings (OpenAI, Cohere, Vertex AI), hybrid / BM25 retrievers, rerankers (Cross-Encoder)Multi-Agent FrameworksCrew AI / AutoGen / LangGraph / MetaGPT / Haystack Agents, planning & tool-use patternsTesting & QualityUnit / integration testing (pytest), guardrails, hallucination tests, behavioral evalsSecurity & ComplianceLeadership & Pre-Sales Experience :
Proven track record shipping ML products at scaleLead 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 LearningEngineer / Azure AI Engineer Associate and Kubernetes CKA / CKAD.Experience in regulated industries (Fintech, Healthcare, eCommerce) is a plus.(ref : hirist.tech)
