AI/ML Solution Architect

Solution Architect AI/ML

Architectural Design & Enterprise Solutions

-Design and implement robust, scalable architectures for AI/ML applications, including LLMs, RAG pipelines, agentic orchestration, and vector databases with built-in guardrails for security, compliance, and responsible AI practices.

- Define and maintain enterprise-level blueprints, technical roadmaps, and solution designs.

- Translate business needs into secure and efficient AI-driven architectures.

Proposals, PoVs & Solutioning:

- Build and present proposals, PoCs, and Proof-of-Value (PoV) decks to showcase solution feasibility.

- Lead technical discussions with stakeholders and prospects, aligning architecture to their goals.

- Provide estimations, resource planning, and end-to-end technical solution outlines.

Implementation & Delivery Oversight

- Oversee end-to-end development and integration of AI solutions across pipelines, APIs, and databases ensuring governance, auditability, observability and ethical adherence in production-grade deployments.

- Provide technical guidance and code/design reviews to ensure maintainable, high-quality systems.

- Guide teams in implementing multi-model inferencing pipelines and agentic orchestration frameworks for enterprise-grade AI applications.

Optimization & Performance

- Identify and resolve performance bottlenecks across AI/ML pipelines.

- Continuously monitor and tune architectures for high-performance operations.

Documentation & Knowledge Sharing

- Maintain comprehensive documentation: architecture diagrams, API specifications, deployment guides.

- Conduct knowledge-sharing sessions and mentor team members on AI/ML and architectural best practices.

Required Skills & Experience

- 10+years of overall technology experience, with 3+ years in AI/ML architecture leadership.

- Proven expertise in Generative AI, LLMs, RAG pipelines, agentic orchestration frameworks, and vector databases.

- Strong background in Python frameworks, API development, and multi-model inferencing.

- Deep knowledge of NLP techniques: transformer networks, embeddings, intent recognition, and prompt engineering techniques.

- Experience with at least one major cloud platform (AWS, Azure, or GCP).

- Strong analytical, architectural sketching, and problem-solving skills.

- Excellent communication and collaboration abilities, with experience mentoring teams.

Preferred Attributes

- Experience in rapid prototyping of AI applications and building solution blueprints independently.

-Familiarity with regulated industries (Healthcare, Pharma, Life science) is a plus, though not mandatory.

-Strong ability to align architectures with business outcomes and drive innovation from concept to delivery.