- Strong hands-on capability in building GenAI prototypes using Python or similar, including prompt engineering, orchestration, and basic backend integration.

- Deep understanding of GenAI concepts : LLMs vs SLMs, RAG architectures, vector databases, agents and tool-calling, prompt design, and evaluation.

- Practical experience with AWS, especially Bedrock, and familiarity with broader AWS data and integration stack (e.g., S3, Lambda, API Gateway, Glue, Redshift/Snowflake/Databricks, Step Functions).

- Ability to understand complex data ecosystems : structured/unstructured sources, data pipelines, APIs, downstream analytical and transactional applications.

- Strong business acumen: can analyze processes, identify automation opportunities, model value levers, and calculate ROI / TCO for AI initiatives.

- Proven experience in customer-facing roles: workshops, demos, PoCs, handling objections, and aligning multiple stakeholders in enterprise accounts.

- Excellent communication and storytelling skills; able to translate low-level technical details into business outcomes for CXO and line-of-business audiences.

- Experience in consultative / solution selling motions alongside account executives or partner sales teams.

Preferred qualifications :

- Prior experience in BFSI or other process-heavy industries (e.g., manufacturing, logistics, healthcare) with an understanding of typical business processes and KPIs.

- Background in data science or ML engineering, with exposure to classical ML as well as GenAI.

- Hands-on experience with one or more : LangChain, LlamaIndex, agent frameworks, vector DBs (e.g., Pinecone, OpenSearch, Redis, Qdrant), and MLOps/LLMOps tooling.

- Certifications in AWS (Solutions Architect, Machine Learning Specialty, or similar) or equivalent cloud credentials.

- Experience working with or for cloud / consulting partners, including building reusable accelerators, blueprints, and industry playbooks.