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Embassy Group, founded in 1993 and headquartered in Bangalore, is one of Indias leading real estate developers with a diversified portfolio spanning commercial office spaces, luxury residential projects, hospitality,retail, and education. It is known for landmark developments such as Manyata Business Park and Embassy GolfLinks, and for pioneering Indias first listed REIT Embassy Office Parks. With a strong focus on sustainability, renewable energy, and community development, Embassy Group continues to shape Indias urban landscape while maintaining global standards in design and execution
Job Title / Position: Assistant Manager - AI Factory
Job Purpose:
Build, deploy, and scale production-grade AI solutions — combining full-stack engineering, LLM capabilities, and enterprise system integration. You will translate business problems into spec-driven, deployable AI products, integrate with enterprise applications, and continuously improve models, workflows, and user experiences.
This position will own the full lifecycle: problem definition → solution design → deployment → iteration — operating at the intersection of engineering, AI, and business.
Key Responsibilities:
1. AI / LLM ENGINEERING
- Hands-on experience with LLMs including ChatGPT and Claude — covering prompt engineering, evaluation, and optimisation.
- Skill Driven Designing & Development & Deployment of Custom Applications
- Strong AI and ML literacy: clear understanding of core concepts, limitations, and trade-offs, with the ability to communicate value directly to business stakeholders.
- Deep expertise in Retrieval Augmented Generation architectures, including embeddings, vector databases (Pinecone, Weaviate, FAISS), and retrieval optimisation.
- Experience designing context-aware AI applications with grounding, memory, and tool usage.
- Familiarity with model evaluation frameworks, hallucination mitigation, and response quality tuning.
2. MULTI-AGENT SYSTEMS & ORCHESTRATION
- Experience with modern agentic frameworks including Microsoft AutoGen, OpenAI frameworks, CrewAI, and LangGraph.
- Ability to design and orchestrate multi-agent systems where autonomous agents collaborate to execute complex workflows and decision-making.
- Strong understanding of agent communication, tool usage, and task decomposition strategies.
3. FULL-STACK DEVELOPMENT
- Proficiency in backend technologies (Python, Node.js) and frontend frameworks (React, Next.js, or equivalents).
- Experience building API-first, microservices-based architectures.
- Strong understanding of SQL and NoSQL database systems and data modelling.
- Ability to integrate AI services into user-facing applications and enterprise workflows without friction.
4. SPEC-DRIVEN & AI-NATIVE DEVELOPMENT
- Experience with spec-driven development using structured prompts, schemas, and contracts to ensure predictable AI outputs.
- Ability to leverage AI tools such as ChatGPT for code generation, debugging, and rapid prototyping.
- Familiarity with structured outputs — JSON schemas, function calling, tool usage — along with validation pipelines.
- Understanding of test-driven approaches for AI systems, including prompt testing, output validation, and guardrails.
5. FORWARD DEPLOYMENT ENGINEERING
- Ability to work closely with business teams to rapidly deploy, customise, and iterate AI solutions in live environments.
- Experience integrating AI systems with enterprise platforms including ERP, CRM, and internal tools.
- Strong problem-solving mindset; able to operate effectively in ambiguous, high-velocity environments.
- Clear ownership of the deployment lifecycle from pilot through to production and scale.
6. AI PLATFORM & MCP SERVER DEVELOPMENT
- Experience building or extending MCP (Model Context Protocol) servers or equivalent orchestration layers for enterprise AI applications.
- Ability to expose enterprise data and workflows securely to LLMs via APIs and tools.
- Understanding of tool orchestration, agent frameworks, and multi-step reasoning pipelines.
7. DATA STRATEGY & CONTEXT ENGINEERING
- Strong understanding of data strategy for AI systems — structuring, chunking, indexing, and retrieval optimisation.
- Ability to design context engineering strategies covering context window management, relevance tuning, and balancing latency with accuracy.
- Experience working with structured and unstructured enterprise data for AI consumption.
8. RAPID PROTOTYPING & EXPERIMENTATION
- Experience with low-code, no-code, and open-source frameworks including LangChain, LangGraph, CrewAI, and Lindy for rapid prototyping and iteration.
- Ability to build POCs and MVPs quickly, validate use cases, and transition them into production-ready systems.
9. SECURITY & GOVERNANCE
- Strong understanding of role-based access control and secure data access patterns.
- Experience implementing data privacy, governance, and audit mechanisms in AI systems.
- Awareness of enterprise security standards and compliance requirements.
10. DEVOPMENT & SCALABILITY
- Experience with cloud platforms (AWS, Azure, GCP) and containerization using Docker and Kubernetes.
- Knowledge of CI/CD pipelines, monitoring, and logging for AI systems.
- Ability to design systems for scalability, reliability, and sustained performance.
Qualifications and Work Experience:
- Bachelor’s degree in IT, Computer Science or related discipline.
- 4 – 6 years of experience in IT infrastructure or network support.
Knowledge, Skills and Competencies:
- Ability to translate business requirements into clear technical specifications and AI workflows.
- Strong communication skills; able to work effectively with cross-functional stakeholders at every level.
- Ownership mindset focused on delivering measurable business outcomes — not just prototypes.
Click on Apply to know more.
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