Company Description

Kotak Securities Limited is one of India's leading financial services organizations and a subsidiary of Kotak Mahindra Bank. The company provides investment, trading, and wealth management solutions to clients across India. With a strong focus on technology, innovation, and customer experience, Kotak Securities continues to invest in scalable digital platforms, artificial intelligence, and modern engineering capabilities.

The organization offers long-term career opportunities, structured growth, and exposure to large-scale technology systems within a regulated financial services environment.

Role Description The Lead, AI and Machine Learning Engineering will lead the design, development, and management of enterprise AI and machine learning platforms at Kotak Securities. This full-time role is based in Mumbai or Bengaluru and reports to the Head of Data. The ideal candidate will have 10–15 years of experience in software engineering, data engineering, or AI/ML systems, including strong engineering leadership experience.

Key responsibilities include building scalable AI platforms, developing LLM-powered applications and agentic workflows, implementing retrieval-augmented generation (RAG) architectures, and establishing robust MLOps, evaluation, and model governance frameworks. The role will also oversee applied machine learning initiatives across personalization, client intelligence, risk management, surveillance, and servicing automation. The lead will be responsible for AI infrastructure, model serving, inference optimization, data security, regulatory compliance, and production reliability while building and managing a high-performing AI engineering team.

The role involves close collaboration with business, risk, compliance, and information security teams to drive enterprise-wide AI adoption.

Qualifications

- 10–15 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.
- Strong Python programming skills, including the ability to develop, review, and maintain production-grade AI and machine learning code.
- 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.
- Strong knowledge of cloud-based AI and machine learning infrastructure, particularly AWS Bedrock, Amazon SageMaker, or equivalent platforms.
- Experience working with modern data platforms and lakehouse architectures using Databricks, Apache Spark, Delta Lake, or comparable technologies.
- 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.
- Experience evaluating and selecting AI technologies, including foundation model providers, vector databases, orchestration frameworks, and MLOps tools based on performance, cost, scalability, and vendor lock-in.
- 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.
- Familiarity with GPU infrastructure, distributed inference, memory optimization, scheduling, and high-throughput model serving is beneficial.
- 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.
- Solid technical leadership, problem-solving, stakeholder management, communication, and cross-functional collaboration skills.

📌 Lead AI and ML Engineer | CTC Upto 65 LPA (Bengaluru)
🏢 Round1 Jobs
📍 Bengaluru