Lead Engineer

Hackajob; Hyderabad, Pune District, Bengaluru; Hybrid; INR 1,800,000 - 2,400,000; Full time

Job summary

Hackajob is seeking a Senior AI Engineer to design scalable AI workflows and integrate autonomous agents into real-world apps. You will architect production-ready AI solutions, implement MCP patterns, and mentor other engineers while focusing on governance, safety, and performance.

Your role includes evaluating new AI tools, building end-to-end RAG and vector-search pipelines, and collaborating with product, data, and software teams to deliver reliable AI features at scale.

Qualifications

• 6–8 years in software development with mentorship and team leadership.

Responsibilities

• Architect and Design production-ready applications leveraging Generative AI and agentic AI frameworks.

Skills

Python

Backend engineering

Mentorship

RESTful APIs

LLM APIs

RAG pipelines

AI governance

Distributed systems

Tools

Docker

Kubernetes

Git

CI/CD

Vector databases

Knowledge retrieval architectures

Job description

You will design scalable AI workflows, integrate autonomous agents into real-world applications, and help translate cutting‑edge AI capabilities into reliable business solutions.

Responsibilities

• Architect and Design production-ready applications leveraging Generative AI and agentic AI frameworks.
• Build, Deploy and Monitor intelligent workflows using LLMs, multi-agent coordination, and orchestration pipelines.
• Integrate new AI applications with traditional pre-existing applications.
• Implement prompt engineering strategies, retrieval-augmented generation (RAG), and contextual memory systems.
• Implement various AI coding techniques (context-driven, spec-driven development etc.).
• Architect and implement AI agents capable of reasoning, planning, and multi-step task execution.
• Implement and utilize Model Context Protocol (MCP) patterns to enable structured communication between agents, tools, and systems.
• Provide mentoring and technical guidance to developers and other AI Engineers.
• Develop evaluation pipelines to measure accuracy, safety, and performance of AI systems.
• Optimize latency, cost efficiency, and scalability of AI-powered workflows.
• Collaborate with product managers, data teams, and software engineers to deliver AI features.
• Ensure reliability through testing, logging, monitoring, and observability of AI behavior.
• Own architectural decisions, coding standards, and best practices.
• Act as an AI trailblazer for development teams by promoting best practices, reusable patterns, and adopting AI-driven engineering approaches.
• Apply AI governance principles to ensure responsible model usage, auditability, and transparency in AI workflows.
• Support implementation of governance controls related to data handling, model behavior monitoring, and risk mitigation.
• Research emerging AI tools and frameworks to continuously improve system capabilities.
• Document AI workflows and maintain reproducible engineering processes.

Qualifications

• 6-8 years of professional software development experience (Dotnet/React/Full-stack) with mentorship/team leadership experience.
• Minimum 2 years of hands‑on experience building applications using Generative AI or agentic AI systems.
• Strong proficiency in Python and backend engineering principles.
• Experience designing RESTful APIs and scalable service architectures.
• Practical experience with LLM APIs, embeddings, vector search, or RAG pipelines.
• Working knowledge of AI governance principles, responsible AI practices, or compliance considerations.
• Familiarity with agent frameworks and autonomous decision workflows.
• Understanding of data structures, system design, and performance optimization.
• Strong analytical thinking and problem-solving skills.

Preferred Qualifications

• Experience deploying AI applications in cloud environments (preferably AWS).
• Familiarity with vector databases and knowledge retrieval architectures.
• Experience working with Git, CI/CD pipelines, and production deployments.
• Exposure to containerization and orchestration tools (Docker, Kubernetes).
• Experience implementing evaluation or monitoring pipelines for AI agents.
• Knowledge of AI safety, guardrails, and responsible AI practices.
• Knowledge of distributed systems or event-driven architecture.