AI Technical Lead
- Piparia Ramban, India
Posted 2 days ago
About the role
Driving Solution Delivery in partnership with Program Managers & AI Principal Architects
- Own end-to-end delivery of AI solutions from design handoff through production deployment, keeping momentum and quality high throughout. Works in partnership with Program Managers to deliver on roadmap timelines that falls within a PI or across multiple PIs.
- Translate solution designs received from Principal AI Solution Architects into sequenced, actionable engineering plans the team can execute immediately
- Track delivery progress across workstreams, identify risks early, and intervene proactively to keep solutions on track. Reports progress to program managers to keep delivery on track. Supports Program Managers in sizing & sequencing of the work and identifying dependencies.
- Make day-to-day implementation decisions within the scope of the agreed architecture, escalating only those decisions that could alter the solution's design or scope
- Coordinate across engineering, data, platform, and QA teams to resolve cross-functional dependencies and remove delivery blockers
- Define and enforce 'done': production readiness criteria, acceptance conditions, and deployment gates for each solution component
- Strong understanding of authentication, authorization, and Identity & Access Management (IAM) concepts, including role-based access controls, service identities, and enterprise security integration.
- Experience designing and implementing solutions that leverage cross-cloud integrations, secure network communications, APIs, and connectivity across cloud and enterprise platforms.
- Working knowledge of cloud governance, security frameworks, compliance controls, and operational best practices for enterprise-scale AI and data platforms.
- Experience implementing security controls for AI/ML applications, data protection, secret management, and secure access to enterprise systems.
- Familiarity with observability and monitoring frameworks, including logging, metrics, tracing, alerting, and platform performance monitoring to ensure reliability and operational excellence. Working Directly With Engineers
- Work alongside engineers daily -- pairing on hard problems, reviewing designs before they become code, and reviewing code before it ships
- Provide engineers with the technical context, design rationale, and decision support they need to move fast without guessing
- Translate high-level solution architecture into component-level and interface-level designs that engineers can implement directly
- Identify when an engineer is stuck, heading in the wrong direction, or about to make a costly mistake -- and step in immediately
- Break down complex solution components into well-scoped tasks that individual engineers can pick up and complete with confidence
- Run focused technical working sessions (design reviews, implementation walkthroughs, debugging sessions) to keep the team aligned and unblocked
- Serve as the primary technical escalation point for engineers before issues are raised to Principal Architects or program managers Hands-On Engineering Contribution
- Write production-quality code for the most complex, highest-risk, or most technically ambiguous parts of each solution
- Build shared utilities, integration layers, and implementation patterns that accelerate the broader engineering team
- Perform deep code reviews with a focus on correctness, maintainability, performance, and alignment with the solution design
- Lead debugging and root-cause analysis for complex issues across ML pipelines, APIs, and data systems
- Validate solution components through testing, profiling, and hands-on verification before sign-off Technical Quality & Production Readiness
- Own the quality bar for every solution delivered -- ensuring components are tested, observable, documented, and ready for production operation
- Establish and enforce testing standards: unit, integration, and end-to-end coverage for AI/ML pipelines and APIs
- Implement MLOps practices for each solution: experiment tracking, model versioning, CI/CD pipelines, monitoring hooks, and alerting
- Ensure operational runbooks, deployment guides, and post-deployment support documentation are produced as part of every delivery Communication & Stakeholder Alignment
- Maintain a clear, current view of delivery status, risks, and decisions -- and communicate it proactively to Principal Architects, Program managers, and other stakeholders
- Own end-to-end delivery of AI solutions from design handoff through production deployment, keeping momentum and quality high throughout. Works in partnership with Program Managers to deliver on roadmap timelines that falls within a PI or across multiple PIs.
- Translate solution designs received from Principal AI Solution Architects into sequenced, actionable engineering plans the team can execute immediately
- Track delivery progress across workstreams, identify risks early, and intervene proactively to keep solutions on track. Reports progress to program managers to keep delivery on track. Supports Program Managers in sizing & sequencing of the work and identifying dependencies.
- Make day-to-day implementation decisions within the scope of the agreed architecture, escalating only those decisions that could alter the solution's design or scope
- Coordinate across engineering, data, platform, and QA teams to resolve cross-functional dependencies and remove delivery blockers
- Define and enforce 'done': production readiness criteria, acceptance conditions, and deployment gates for each solution component
- Strong understanding of authentication, authorization, and Identity & Access Management (IAM) concepts, including role-based access controls, service identities, and enterprise security integration.
- Experience designing and implementing solutions that leverage cross-cloud integrations, secure network communications, APIs, and connectivity across cloud and enterprise platforms.
- Working knowledge of cloud governance, security frameworks, compliance controls, and operational best practices for enterprise-scale AI and data platforms.
- Experience implementing security controls for AI/ML applications, data protection, secret management, and secure access to enterprise systems.
- Familiarity with observability and monitoring frameworks, including logging, metrics, tracing, alerting, and platform performance monitoring to ensure reliability and operational excellence.
Education
- BS/MS in Computer Science, Machine Learning, Software Engineering, or equivalent combination of education and experience. Certification / License
- Relevant certifications such as AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate. Work Experience
- 4+ years of hands-on AI/ML engineering experience with a strong record of shipping production solutions
- Demonstrated ability to drive end-to-end delivery of complex AI/ML solutions working directly with engineering teams
- Minimum of 4 years of deep data engineering knowledge and experience
- Deep proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, JAX)
- Experience implementing solutions from architectural designs produced by senior architects
- Proven ability to guide engineers through design and implementation decisions in real time
- Strong understanding of the full AI/ML solution stack: data pipelines, model training, serving, monitoring, and APIs
- Solid command of MLOps practices: CI/CD for models, experiment tracking, deployment pipelines, production monitoring
- Clear, direct communication style with both technical and non-technical stakeholders Knowledge / Skills / Abilities
- Must be deeply curious, desire to experiment
- Expertise in machine learning algorithms, Decision trees, Business dynamic models, Agent based models, Advanced statistical techniques and operations research
- Strong proficiency in programming languages such as Python, R, and Java
- Ability to design scalable, secure, and efficient solutions with strong data foundations
- Exceptional problem-solving and analytical skills
- Strong leadership and mentorship abilities, leading with influence (without authority), with a focus on fostering innovation, teamwork and collaboration
- Excellent communication skills, capable of translating technical concepts to diverse audiences
- Ability to work in a fast-paced, dynamic environment and manage multiple priorities
Education
- Master’s degree in Artificial Intelligence, Data Science, or a closely related field with significant research or project work in AI or machine learning
- Relevant certifications such as AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate
- Experience with AI/ML, generative AI and reinforcement learning
- Experience with LLMs, generative AI, RAG pipelines, or applied NLP/computer vision solution delivery
- Familiarity with cloud AI platforms such as AWS SageMaker, GCP Vertex AI, Azure ML and container orchestration
- Background in API design and microservices integration for AI/ML systems
- Experience working in delivery-focused squads or cross-functional product teams
- Track record of improving engineering team velocity or quality through direct technical involvement
- Familiarity with DevOps practices and MLOps pipelines for AI deployment
- Experience in industries such as manufacturing, finance or technology