Lead, AI And Machine Learning Engineer | CTC Upto - 60-65LPA

Company Description

Kotak Securities Limited, a subsidiary of Kotak Mahindra Bank, is undertaking a technology transformation across its trading, risk, data, and infrastructure platforms. The company is building an AI platform to support business-wide adoption within a regulated financial services environment. With a business-funded, multi-year technology roadmap, Kotak Securities offers opportunities to build reliable systems, solve complex engineering challenges, and grow within a long-term, in-house engineering team.

Role Description

This is a full-time role based in Bangalore for a Lead, AI and Machine Learning Engineering, reporting to the Head of Data. Designed for professionals with 10–15 years of experience, the role involves owning AI platform architecture and applied machine learning initiatives across personalisation, client intelligence, risk and surveillance, and servicing automation. Responsibilities include model routing, agent orchestration, retrieval infrastructure, inference optimisation, evaluation, and end-to-end MLOps on the lakehouse. The role will establish standards for model governance, reproducibility, security, and data privacy while meeting regulatory and audit requirements. The person will build and lead the engineering team, guide technology selection, and collaborate with Business, Risk, Compliance, and Information Security to deliver reliable AI capabilities across the organisation.

Qualifications

• 10 or more years in software or data engineering, including at least 5 years focused on AI or machine learning systems and 3 years leading engineers.
• Proven experience building and operating production ML systems at scale, including evaluation, monitoring, and performance under degraded conditions.
• Hands-on expertise in large language models, retrieval-augmented generation, agent orchestration, tool use, prompt engineering, structured outputs, and function calling.
• Experience with parameter-efficient fine-tuning, quantitative evaluation, automated testing, and human review processes.
• Strong Python programming skills and familiarity with modern inference stacks and model-serving frameworks.
• Experience with AWS Bedrock, SageMaker, or equivalent cloud ML platforms, alongside lakehouse technologies such as Databricks, Spark, and Delta.
• Strong understanding of MLOps, including feature infrastructure, training and inference pipelines, model registries, lineage, drift detection, and retraining.
• Experience incorporating model risk, bias and safety testing, explainability, human oversight, and auditability into system design.
• Ability to assess AI use cases, make informed build-versus-buy decisions, and balance performance, cost, and vendor dependency.
• Demonstrated ability to lead engineering teams and collaborate with business, risk, compliance, and security stakeholders.
• Financial services or capital markets experience, particularly in surveillance, credit, or risk modelling, is preferred.
• Familiarity with DPDPA, SEBI CSCRF, or comparable frameworks, GPU inference optimisation, and organisation-wide AI adoption is an advantage.