NCS Group
Website:
ncs.co
Job details:
NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group. For more information, visit ncs.co.
Looking for your next big challenge in AI? #NCSIndia is scaling our team of AI Professionals; we’re seeking AI Engineers, AI Leads, and Architects who are passionate about the intersection of robust design and real-world impact. If you thrive on solving complex technical challenges and want to be at the forefront of strategic AI implementation, let's connect.
Job Title: AI Engineer - Agentic & GenAI Systems
Role Summary
Design, build, and operate production-grade agentic and GenAI systems from end to end. You will ship robust APIs, reusable components, and secure pipelines that connect Large Language Models (LLMs) with enterprise systems. This role requires pairing strong software engineering with modern AI practices—such as RAG, agent orchestration, and evaluation—to deliver scalable business outcomes.
Responsibilities
Agent & Application Engineering
- Multi-Agent Systems (MAS): Design systems involving planning, tool-use, and delegation using frameworks like LangGraph or Semantic Kernel.
- API Development: Develop and expose agentic workflows via REST/gRPC APIs. Proficiency in FastAPI is mandatory.
- Model Context Protocol (MCP): Integrate tools, SQL, search, and document stores using MCP with strict type contracts and safe sandboxes. Focus is on the consumption of MCP servers rather than building them from scratch.
- Model Gateway Integration: Integrate with model gateways (OpenAI, Azure OpenAI, Bedrock, or Vertex AI). Knowledge of at least one of these platforms is required.
- Pro-Code Development: Build solutions using high-level coding (Python-focused); low-code/no-code experience alone is not sufficient.
Retrieval, Data & Knowledge
- RAG Services: Stand up Retrieval-Augmented Generation services, including chunking, enrichment, embeddings, and indexing using hybrid/vector search (e.g., pgvector, Pinecone, Weaviate, OpenSearch).
- Ingestion Pipelines: Implement ingestion pipelines for diverse data sources like documentation, tickets, and CRM data using Airflow, Prefect, or Ray.
- Optimization: Continuously optimize retrieval quality through chunking strategies, re-rankers, and query rewriting based on evaluation metrics.
Quality, Testing & Evaluation
- Evaluation Frameworks: Utilize Promptfoo and RAGAs for evaluating LLM outputs.
- Testing Mindset: Treat prompts and graphs as code—version, diff, and test them using golden sets and regression suites.
- Metrics Awareness: Maintain a strong awareness of AI evaluation metrics and how to verify RAG applications.
Security & Compliance
- Red Teaming: Maintain familiarity with red teaming guardrails for AI systems.
- Guardrails: Implement policy chains and guardrails using tools like OPA/Gatekeeper or Presidio for PII redaction.
Tech Stack & Qualifications
- Primary Language & Frameworks: Python and FastAPI are mandatory.
- Agent Frameworks: LangGraph or Semantic Kernel.
- Cloud Platforms: Experience in any one major platform (Azure, AWS, or GCP).
- Operations (Optional): Packaging services as containers and deploying to Kubernetes with Helm/Argo CD is a bonus but not mandatory. Platform-level concerns like tenant isolation are handled by a separate team.
- Secondary Languages (Optional): Knowledge of Java, Go, or Node.js is considered an advantage but not required.
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