Lead AI and ML Engineer

About the Role

Lead the design, development, and management of enterprise AI and machine learning platforms at Kotak Securities. This full-time role is based in Mumbai and reports to the Head of Data.

Key Responsibilities

• Build scalable AI platforms.
• Develop LLM-powered applications and agentic workflows.
• Implement retrieval-augmented generation (RAG) architectures.
• Establish robust MLOps, evaluation, and model governance frameworks.
• Oversee applied machine learning initiatives across personalization, client intelligence, risk management, surveillance, and servicing automation.
• Be responsible for AI infrastructure, model serving, inference optimization, data security, regulatory compliance, and production reliability.
• Build and manage a high-performing AI engineering team.
• Collaborate closely with business, risk, compliance, and information security teams to drive enterprise-wide AI adoption.

Requirements & Tech Stack

• Python programming skills.
• Cloud-based AI and machine learning infrastructure, particularly AWS Bedrock, Amazon SageMaker, or equivalent platforms.
• Modern data platforms and lakehouse architectures using Databricks, Apache Spark, Delta Lake, or comparable technologies.
• Experience evaluating and selecting AI technologies, including foundation model providers, vector databases, orchestration frameworks, and MLOps tools.
• Familiarity with GPU infrastructure, distributed inference, memory optimization, scheduling, and high-throughput model serving is beneficial.

Qualifications

• 0–15 years of experience in software engineering, data engineering, or AI/ML systems, including at least 5 years focused on AI or machine learning and 3 years of engineering leadership experience.
• Deep hands-on expertise in building and deploying production-grade machine learning systems at scale, including model development, deployment, monitoring, and performance optimization.
• Strong experience in large language model (LLM) engineering, including retrieval-augmented generation (RAG), agentic AI systems, tool use, prompt engineering, context management, and function calling.
• Expertise in designing AI platform architectures, including model gateways, routing, agent orchestration, state management, multi-tenancy, quota management, and guardrail frameworks.
• Proven experience building advanced retrieval systems using document processing, chunking, embeddings, vector databases, hybrid search, reranking, and citation-based grounding.
• Hands-on experience with LLM inference and serving frameworks, including managed model endpoints, self-hosted inference, batching, caching, quantization, and latency optimization.
• Expertise in AI model evaluation, including task-level benchmarking, automated evaluation pipelines, LLM-as-a-judge frameworks, human evaluation, regression testing, and production quality monitoring.
• Experience building MLOps infrastructure, including feature stores, training and inference pipelines, model registries, experiment tracking, model lineage, drift detection, and automated retraining.
• Proven experience developing machine learning solutions for personalization, recommendation systems, customer intelligence, risk analytics, fraud detection, or surveillance.
• Strong understanding of reproducibility, model versioning, feature consistency, prediction traceability, and production ML monitoring.
• Experience implementing AI governance frameworks, including model risk management, bias detection, safety testing, explainability, human-in-the-loop workflows, and auditability.
• Knowledge of data governance, privacy, consent management, access controls, data retention, and regulatory requirements such as DPDPA and SEBI CSCRF.
• Strong understanding of AI infrastructure cost optimization, including token usage, semantic caching, inference throughput, GPU utilization, and model serving efficiency.
• Proven ability to lead AI engineering teams, establish engineering standards, manage technical roadmaps, and deliver production-ready AI solutions.
• Strong architectural decision-making skills and the ability to evaluate when AI-based solutions are appropriate versus traditional software, rules-based systems, or simpler automation.
• Prior experience in banking, capital markets, financial services, or other regulated industries is preferred.
• Experience developing AI/ML solutions for financial risk modelling, trading analytics, market surveillance, credit assessment, or fraud detection is advantageous.
• Experience driving enterprise-wide AI adoption, stakeholder alignment, and change management across large organizations is highly desirable.
• Published research, open-source contributions, conference presentations, or demonstrated contributions to the AI/ML engineering community are advantageous.
• Strong technical leadership, problem-solving, stakeholder management, communication, and cross-functional collaboration skills.