Expectation for the role

How to connect integrated systems through APIs and MCPs
We need candidates who have done REST APIs and gRPCs for large enterprise and connected multi systems to deliver the digital outcome of the application. This candidate working in the RAG development, Al agent development in the past 1 to 3 years will be a better fit than person who have created ML models and inferencing and fine tuned ML models and created new models and have an experience of RAG & Agents.
The person MUST have the skill of orchestrating, integrating and processing multi system connections through API and MCP along with the knowledge of Al agents frameworks, RAG and LLMs
Ability to coordinate multi system environment.

JD for Al Engineer role

Experience building agent-based architectures, such as tool-using agents, workflow agents, multi-agent collaboration systems, and autonomous/goal-seeking pipelines using frameworks like LangChain, Llamalndex, Haystack, or custom orchestration layers.
Experience designing, architecting, and deploying end-to-end Al systems, with a deep focus on building agentic workflows, autonomous orchestration patterns, and multi-step reasoning pipelines that integrate reliably with production systems.
Proficiency with large language models (LLMs) and modern Al frameworks, including hands-on experience with model APIs, fine-tuning techniques, retrieval- augmented generation (RAG), function calling, tool use, and structured output enforcement.
Advanced prompt engineering capabilities, including development of reusable prompt templates, prompt-chaining patterns, system instruction design, context management, and optimization of prompts for accuracy, determinism, and cost efficiency.
Deep understanding of retrieval and knowledge systems, including vector databases (e.g., Pinecone, Weaviate, Chroma, Vertex Matching Engine), embedding model selection, chunking strategies, and indexing techniques for low-latency, high-recall retrieval.
Designing Al workflows that integrate with business systems, including function-calling APIs, microservices, internal tools, CRMs, productivity suites, cloud tasks, and event-driven architectures.
Strong grounding in model evaluation and safety, including development of automated evaluation harnesses, guardrail systems, toxic output mitigation, hallucination reduction, and structured monitoring for output correctness and drift.
Experience with production-grade Al infrastructure, including serverless orchestration, containerized deployments, GPU/accelerator environments, and CI/CD workflows tailored for LLM-based systems.
Python engineering skills, including writing clean, testable, maintainable code, and building robust libraries, utilities, and agent behaviors that support long-running or complex Al workflows.
Hands-on experience with cloud-native Al tooling, such as: a. AWS: Bedrock, Sagemaker, Lambda, Step Functions, DynamoDB, S3, API Gateway b. GCP: Vertex Al, Cloud Functions, Cloud Run, Pub/Sub, BigQuery, GKE with strong understanding of security, identity, networking, and cross-service orchestration for Al-heavy systems.
Experience building monitoring and observability layers for Al systems, including prompt/response logging, latency tracking, quality scoring, conversation replay, and anomaly detection for agent performance.

Senior Agentic AI Engineer