Senior AI Solutions Lead

Role & responsibilities

Experience: 812+ years (incl. 3–5+ years leading AI/ML or LLM initiatives)

Location - Bangalore / Hyderabad

Role Summary

We're seeking a Senior AI Solutions Lead to operate as a trusted advisor to business, solution architects, and engineering teams. You'll set product/technical direction, drive system design and delivery for AI solutions, uplevel cross-functional teams, and ensure high-quality outcomes. This role blends strategy & governance, architecture & design, enablement & upskilling, and hands-on technical leadership (including code reviews and starter implementations when needed).

What You'll Do

Strategy & Advisory

Lead discovery and requirements with Business, Solution Architecture, and Development stakeholders; translate objectives into clear technical plans and trade-offs.

Define and maintain the project roadmap, milestones, and delivery timelines; manage risks, dependencies, and scope.

Shape the governance model for AI projects (review gates, quality bars, approval flows).

Architecture & Design

Define end-to-end system architecture for AI applications, including service boundaries, communication contracts between subsystems, and data flows.

Select patterns for LLM-based systems (prompting strategies, structured outputs, evaluation loops), including non-RAG approaches where appropriate.

Establish standards for reliability, security, and compliance.

Delivery Leadership

Convert business goals into work items (Epics/Stories/Tasks) and acceptance criteria; partner with SA/PM to keep Jira current.

Provide daily technical guidance and code reviews to keep teams aligned and efficient; write "starter code" or proof-of-concepts to accelerate delivery when needed.

Drive quality engineering: failure-mode analysis, test strategy, and iterative improvement of prompt/agent performance.

Data, Evaluation & Observability

Lead data analysis to curate representative examples, edge cases, and failure modes for prompts/agents.

Define and implement evaluation & observability (e.g., offline/online evals, drift monitoring, human-in-the-loop, tools like Fiddler or equivalents).

Enablement & Change Management

Design and deliver enablement programs (playbooks, document templates, training) for internal and client teams—technical and non-technical.

Mentor engineers and SAs to increase autonomy; reduce single-threaded reliance on the advisory function.

Team Building

Help identify and onboard an additional AI/ML or Platform Engineer to absorb execution-heavy tasks and reduce advisory bottlenecks.

Required Qualifications

8–12+ years in software/AI/ML, with 3–5+ years leading delivery of AI systems or complex platform initiatives.

Proven record of cross-functional leadership with business, SA, and engineering stakeholders.

Strong systems architecture skills (service design, contracts, interfaces, data pipelines).

Hands-on proficiency in Python and modern AI tooling (prompt engineering, LLM orchestration, evaluation).

Experience defining roadmaps, translating requirements to Jira epics/stories, and running Agile ceremonies.

Demonstrated enablement impact (training, playbooks, templates) and excellent written/verbal communication.

Comfort writing starter code and conducting rigorous code reviews to guide teams.

Nice to Have

Experience with LLMOps/MLOps (evaluation pipelines, offline/online metrics, experiment tracking).

Familiarity with observability tools for AI systems (e.g., Fiddler, Arize, WhyLabs) or equivalent APM/telemetry stacks.

Knowledge of RAG and alternatives, retrieval/indexing strategies, prompt safety, and guardrails.

Cloud & platform experience (Azure, Kubernetes, CI/CD), and integration with data/analytics platforms.

Prior consulting/advisory background; experience upskilling large client teams.

Core Competencies

Trusted Advisory: influence without authority, executive-ready communication, stakeholder trust.

Systems Thinking: clear decomposition, interface design, failure-mode awareness.

Delivery Excellence: predictable execution, risk management, crisp definition of done.

Teaching & Mentorship: elevates team capability; creates repeatable assets.

Bias to Quality: evaluation discipline, measurable improvements, observability.

Success Metrics (first 6–12 months)

Roadmap and milestone adherence; reduction in rework/waste.

Team autonomy increases (fewer daily clarifications; stronger SA/Dev ownership).

Measurable quality gains (eval metrics, failure-rate reduction, user satisfaction).

Adoption of enablement assets (playbooks/templates) across teams.

Successful onboarding of an additional engineer to offload coding/reviews.