Senior AI Engineer

6 to 8 years

Software Developer with strong AI experience who can operate across the full lifecycle of AI solution delivery from infrastructure design to application development to internal enablement. This role blends solid software engineering fundamentals with applied AI expertise including agentic AI systems, AI agents, MCP (Model Context Protocol) integrations, and RAG (Retrieval-Augmented Generation) systems along with solid database skills (PostgreSQL, Snowflake), research curiosity, and a passion for knowledge-sharing across the organization.

Key Responsibilities:

Application Development:

- Design, build, test, and ship robust software applications with an agentic AI-first approach integrating AI agents, MCP-based tool/server integrations, and RAG-based systems into existing or new products.
- Build and connect MCP servers/clients to extend AI agents with tools, data sources, and internal systems.
- Design and manage data models, schemas, and queries using PostgreSQL and Snowflake to support application and AI workloads.
- Collaborate with engineering teams throughout the development lifecycle requirements, design, coding, testing, and deployment.
- Write clean, maintainable, and scalable code following good software engineering practices.

Infrastructure & Scalability:

- Design, evaluate, and set up AI infrastructure to support model training, fine-tuning, and inference workloads, including infrastructure for AI agents, MCP server/tool orchestration, and RAG pipelines (vector databases, retrieval layers, orchestration frameworks).
- Architect and optimize database infrastructure (PostgreSQL for transactional/application data, Snowflake for analytical/warehouse workloads) to support growing scale and performance needs.




- Assess current systems and understand business/technical requirements to plan for scale.
- Recommend and implement architecture improvements to support growing agentic AI and data workloads efficiently.

AI Solution Delivery:

- Deliver timely, production-ready AI solutions aligned with business needs, favoring agentic AI approaches leveraging AI agents, MCP integrations, RAG systems, and well-structured data pipelines from Postgres/Snowflake where applicable.
- Proactively pitch AI-driven solutions and use cases including multi-agent workflows, MCP-enabled tool access, retrieval-augmented approaches, and data-driven insights to stakeholders and cross-functional teams.
- Translate ambiguous problems into practical, deployable, agent-driven AI features.

Research & Tooling:

- Continuously research internal AI tools, platforms, and documentation to stay current with available capabilities with particular attention to agentic AI frameworks, MCP servers/protocols, RAG architectures, and evolving database/data platform tooling.
- Evaluate new internal/external AI tools and assess their applicability to ongoing projects, especially in the agentic AI, MCP, and data ecosystem.
- Maintain and share up-to-date knowledge of internal AI tooling with the broader team.

Knowledge Sharing & Enablement:

- Organize and host weekly AI talks/sessions to share learnings, tool updates, and best practices across teams including deep dives on agentic AI, MCP, RAG systems,



and data engineering practices.
- Foster a culture of continuous AI learning within the organization.

Requirements:

- Minimum 5 years of professional software development experience, with significant hands-on exposure to AI/ML.
- Strong programming skills (Python preferred; additional languages a plus) and solid grasp of software engineering fundamentals (data structures, APIs, testing, version control).
- Experience with LLMs, agent orchestration tools/frameworks (e.g., LangGraph, multi-agent orchestration, autonomous workflows), MCP, and RAG pipelines.
- Experience with RAG systems (embeddings, vector stores, retrieval pipelines).
- Proficiency in SQL and hands-on experience with PostgreSQL (schema design, query optimization, transactional systems) and Snowflake (data warehousing, analytics, performance tuning).
- Familiarity with MCP (Model Context Protocol) building or integrating MCP servers/clients to connect AI agents with tools, data, and internal systems.
- Experience with AI infrastructure (cloud platforms, GPU/compute provisioning, MLOps pipelines).
- Ability to independently research, evaluate, and document tools/technologies.
- Strong communication skills comfortable presenting technical concepts to varied audiences.
- Self-driven with a proactive approach to identifying AI opportunities across the business.

Nice to Have:

- Experience with early/traditional NLP techniques (e.g., rule-based systems, TF-IDF, HMMs, n-gram models, classic POS tagging) in addition to modern deep learning-based NLP a strong added advantage.
- Prior experience running internal tech talks, workshops, or communities of practice.
- Exposure to scaling infrastructure for agentic AI and data workloads in production environments.

📌 Senior AI Engineer (India)
🏢 SYSMIND
📍 India