Responsibilities and Impact:
- Design and architect scalable AI/ML platforms and infrastructure that support enterprise-wide AI initiatives, including multi-agent systems, RAG pipelines, and model lifecycle management.
- Lead cross-functional teams (data science, engineering, product, security, compliance) in implementing cloud-native solutions and establishing best practices for AI platform development and operations.
- Drive technical strategy and roadmap planning for AI infrastructure, ensuring alignment with business objectives, risk frameworks, and regulatory requirements.
- Collaborate with data science teams, engineering groups, and business stakeholders to translate AI requirements into robust platform solutions and reusable components (APIs, microservices, SDKs, toolchains).
- Establish governance frameworks, security protocols, and compliance standards for AI/ML workloads in production (model risk management, lineage, auditability, explainability, Responsible AI).
- Mentor and guide technical teams, elevating architectural rigor, code quality, and operational excellence while staying current with emerging AI technologies and industry best practices.
- Oversee the complete lifecycle of AI solutions: data acquisition and curation, feature engineering, model training (SFT, RLHF), evaluation and guardrails, CI/CD, deployment, monitoring, and iterative improvement.
- Define patterns for Agentic AI (planning, tool-use/function calling, memory, feedback loops, multi-agent collaboration), and standardize frameworks for orchestration and evaluation at scale.
- Implement observability and reliability for AI systems (offline/online evals, data/model drift, hallucination/toxicity metrics, cost/performance optimization, SLA/SLO management).
- Champion platform reusability and IP creation through reference architectures, accelerators, and governance-aligned templates.
Architecture Focus:
- Cloud-native AI platforms on AWS/Azure/GCP using Kubernetes, serverless, IaC (Terraform), service mesh, and event-driven architectures (Kafka).
- LLM/SLM stack: RAG (indexing, chunking, retrieval strategies), vector databases (FAISS/Milvus/Pinecone), prompt management, guardrails, caching, redaction, and policy enforcement.
- MLOps/LLMOps: MLflow/Kubeflow/SageMaker/Vertex AI/Azure ML, feature stores, model registry, experiment tracking, automated evaluations (RAGAS, DeepEval), and canary/blue-green deployments.
- Explainability and model risk management: SHAP/LIME, documentation, controls testing, and audit readiness.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related fields; Ph.D. is a plus. Preferably from premier institutes (IITs, IISc, NITs, BITS, IIITs).
- 1218 years of experience in AI/ML and software architecture, including hands-on leadership of large-scale AI platforms in production within financial services or similarly regulated industries.
- Proven experience architecting and deploying Agentic AI solutions and LLM/SLM platforms (SFT, RLHF, RAG) with strong practical knowledge of Python and relevant libraries (Pandas, NumPy, TensorFlow, PyTorch).
- Expertise with orchestration and toolchains: Hugging Face, LangChain/LlamaIndex, AutoGen/CrewAI (or similar), and modern evaluation/guardrail frameworks.
- Strong cloud engineering skills (AWS/Azure/GCP), containerization (Docker, Kubernetes), IaC (Terraform), microservices, and event streaming.
- Experience with SQL and NoSQL data sources; sound understanding of data modeling, governance, and lineage.
- Demonstrated application of secure-by-design principles, Responsible AI practices, and regulatory compliance for production AI workloads.
- Excellent communication and stakeholder management skills; able to translate complex concepts into clear architectural artifacts and executive-friendly narratives.
- Track record of mentoring and building high-performing technical teams; commitment to engineering best practices and operational excellence.
- A strong public profile (Kaggle, open-source contributions, publications) is a plus.
Benefits:
- Hybrid work model with collaborative office culture in Pune or Mumbai.
- Continuous learning support for certifications, workshops, and conferences.
- Opportunity to shape enterprise-wide AI strategy and build differentiated IP in financial services.
