AI/ML Solutions - Engineering Lead

Overview

Leads the designing and implementation of solutions leveraging AI and machine learning techniques to solve complex business problems, providing technical expertise and thought leadership. The role brings architectural clarity to AI/ML initiatives, delivers end-to-end solutions, and leads and mentors a multidisciplinary team.

What you'll do

• Define and govern scalable enterprise AI architectures, standards, reusable patterns, and platform strategies across GenAI, RAG, Agentic AI, and AI platforms.
• Define and evolve the architectural blueprint for AI/ML solutions developed across the AI CoE.
• Identify reusable components, services, and patterns that can be leveraged across platforms and use cases.
• Collaborate with CTO and platform teams to align AI/ML architecture with enterprise technology strategy.
• Establish and promote golden paths for AI/ML development, deployment, and governance.
• Shape the target-state architecture for enterprise AI platforms, AI Control Tower capabilities, and federated AI delivery models.
• Translate business requirements into robust, scalable AI/ML solutions.
• Deliver AI solutions end-to-end from ideation and PoC through production deployment, MLOps/LLMOps, monitoring, and operational support.
• Collaborate with cross-functional teams to integrate AI capabilities into products and platforms.
• Lead the design and implementation of AI/ML solutions from data ingestion and transformation to model development, evaluation, and deployment.
• Apply best practices in MLOps, DevOps, and scalable cloud-native architectures.
• Manage and mentor a team of data scientists, data engineers, and ML engineers.
• Influence business, technology, risk, and architecture stakeholders while leading multidisciplinary teams and driving enterprise AI adoption.
• Influence architectural decisions through clear communication and stakeholder alignment.
• Drive adoption of architectural standards and reusable assets across delivery teams.

What you'll need

• 15+ years of experience.
• Proven experience in AI/ML solution architecture across multiple use cases and platforms.
• Proven experience operationalising AI solutions from PoC to production, including production readiness, monitoring, support models, scalability, and value realisation.
• Deep hands-on experience designing and implementing GenAI, RAG, Agentic AI, LLM-based solutions, and enterprise AI platforms at scale.
• Strong understanding of cloud platforms, preferably Google Cloud Platform (GCP) and Microsoft Azure.
• Strong foundation in statistics, machine learning, and deep learning.
• Proven experience building and deploying GenAI solutions using frameworks like LangChain, LangGraph, or similar.
• Expertise in Natural Language Processing (NLP), including semantic search, entity recognition, and text generation.
• Hands-on experience with LLMs, including GPT, LLaMA, Claude, and Mistral, and fine-tuning/customisation techniques.
• Expertise in designing and implementing scalable MLOps, LLMOps, CI/CD, model lifecycle management, AI observability, and enterprise AI platform capabilities.
• Deep understanding of AI governance, Responsible AI, model assurance, security, privacy, risk management, and regulatory compliance frameworks.
• Experience implementing AI monitoring, model performance management, drift detection, SLA management, and operational reporting.
• Ability to design and implement scalable, reusable, and secure AI/ML components.
• Knowledge of AI security controls including prompt injection protection, data protection, DLP, privacy, and AI guardrails.
• Experience working with cross-functional teams and influencing architectural decisions.
• Strong communication and stakeholder management skills.

Nice to have

• A degree in Computer Science, Data Science, AI/ML, or related field.
• Experience with GenAI, LLMs, and autonomous agent architectures.
• Prior experience in enterprise-wide architectural initiatives involving AI/ML.
• Familiarity in cloud platforms including Azure, AWS, and GCP, and containerisation including Docker and Kubernetes.
• Knowledge of enterprise AI governance, ethical AI, and model interpretability.
• Certification in TOGAF or equivalent enterprise architecture frameworks is a plus.

Details

• Location: Hyderabad.
• Hybrid working.
• The role provides line management and/or coaching to grow team capability.