Overview

Applied AI Engineer – Generative and Agentic AI

Responsibilities

• Work with customers, domain specialists, consultants, and product teams to translate business workflows, data constraints, risks, and success measures into practical AI solution designs.
• Build LLM-powered applications using Python, APIs, prompt and context engineering, embeddings, vector search, RAG, and structured and unstructured enterprise data.
• Develop tool-using and multi-agent workflows with function calling, MCP connectors, memory, human approvals, and AI gateway, model gateway, and agentic gateway controls for secure orchestration and enterprise integration.
• Create evaluation pipelines for relevance, groundedness, task completion, safety, latency, and cost; use automated tests and regression datasets to improve solution quality.
• Apply AI governance, AI security, AI ethics, and compliance controls across the solution lifecycle, including RBAC, PII and DLP protection, guardrails, audit trails, explainability, human oversight, and risk documentation.
• Prototype, containerize, deploy, monitor, troubleshoot, and document AI solutions on AWS or GCP; support demonstrations, user testing, production handover, and reusable Aptus accelerators.
• Deliver production RAG and multi-agent systems with tool permissions, MCP integration, evaluations, monitoring, and human-in-the-loop approvals.
• Conduct threat modelling, prompt-injection and data-leakage testing; optimize hallucination rates, latency, inference costs and task success.
• 3–5 years of software/AI engineering experience, including deployed LLM, RAG or agentic applications and evaluation pipelines.
• Strong Python, SQL, Git, data structures, debugging, and REST API development, with the ability to write clean, testable, and maintainable code.
• Understanding of machine learning and NLP fundamentals, transformers, embeddings, prompting, vector search, RAG, agentic workflows, tool calling, and practical AI evaluation.
• Hands-on project experience with LLM APIs or open-source models, a vector database, and an orchestration framework such as LangChain, LlamaIndex, or Semantic Kernel.
• Working knowledge of FastAPI, Flask, or Node.js, cloud services, Docker, data integration, and AI gateway patterns for authentication, routing, fallbacks, rate limits, budgets, policies, and observability.
• Structured problem solving, ownership, clear technical communication, and the ability to convert business requirements into secure and usable AI solutions.
• Hands-on GitHub and Jira integration experience is mandatory: link Jira issues to branches, commits, pull requests, and CI/CD activity; maintain defect, review, and release traceability.

Qualifications

• Primary Practice- Generative and Agentic AI Engineering
• Work Areas- Product & Platform Development | Data & AI Consulting | Technology Services
• Role Summary

Education

• B.Tech/B.E. in Computer Science, IT or related engineering discipline; M.Tech/M.S. is an advantage. Relevant professional delivery experience, technical ownership, and demonstrable project outcomes are essential.
• 3–5 years of relevant full-time professional experience, including responsibility for production systems, engineering documentation, stakeholder communication and delivery outcomes.
• Exposure to Hugging Face, PyTorch, LangChain, LlamaIndex, Semantic Kernel, vector databases, MCP, MLflow, Docker, Kubernetes, AWS, GCP, or Databricks.
• Familiarity with AI governance, AI security, AI ethics, regulatory and industry compliance, guardrails, PII and DLP controls, explainability, human oversight, AI observability, LLMOps, and responsible AI practices.
• Selection: relevant project screening, practical technical exercise, architecture/problem-solving discussion and delivery/ownership interview.