Walk-in || AI Engineer

Role:

As an AI Engineer, you will build AI and agent features for an enterprise-grade SaaS platform in the data privacy, data governance and AI governance space. Your work will cover agent-based workflows, AI asset discovery and AI guardrails that detect and enforce controls for PII, toxicity and prompt injection, as well as new AI features taken from prototype to production.

Key Responsibilities:

Role Overview :

We are looking for a hands-on Applied AI Engineer to build and maintain components of an enterprise grade, multi-agent AI platform. The ideal candidate has 35+ years of software engineering experience with strong Python skills, practical experience building GenAI/LLM-powered applications, and the ability to implement agent logic, RAG pipelines, and integrations under the architectural direction of the AI Architect. The role requires close collaboration with the Business Analyst, Integration Engineer, DevOps Engineer, and QA teams to deliver a secure, auditable, and production-ready document intelligence system.

Preferred Experience Profile

• Ai Engineer
• OverAll 3+ yrs with 2 years of practical AI/GenAI experience
• Senior Ai Engineer
• Over all 5 to 8 Yrs of Experience ( Java + Python ) with 3+ yrs of Strong Hands-on Ai/GenAI Experience
• Lead Engineer
• 8+ Over all Experience ( Java + Python ) with 4+ yrs of Strong Hands-on Ai/ GenAI Experience Strong focus on implementation, delivery, and problem-solving in production environments Key Responsibilities Agent & AI Application Development
• Build and maintain individual agents (e.g., Proofreader, Formatter, Document Converter, Paralegal, Reviewer/Approver, Document Router) per the architecture defined by the AI Architect.
• Implement multi-agent orchestration logic, task decomposition, tool/function calling, and inter agent communication using frameworks such as LangChain / LangGraph.
• Build Retrieval-Augmented Generation (RAG) pipelines — document chunking, embeddings generation, and vector store integration — for grounding agent responses in SOPs, QC guidelines, and playbooks.
• Implement agent-to-tool and agent-to-system integrations using standardized protocols such as Model Context Protocol (MCP). Required Skills & Experience
• 3–5+ years of overall software engineering experience, including at least 2 years building AI/ML, GenAI, or LLM-powered applications.
• Strong programming skills in Python Or Java with the ability to build and maintain enterprise-grade APIs, microservices, and integration layers.
• Practical experience building or contributing to multi-agent / agentic AI systems, including agent orchestration and autonomous task execution.
• Working knowledge of agent frameworks such as LangChain, LangGraph, or Semantic Kernel, and familiarity with the Model Context Protocol (MCP) for tool/data integration.
• Experience implementing Retrieval-Augmented Generation (RAG) pipelines — embeddings, chunking strategies, and vector databases (e.g., Azure AI Search, Qdrant, Pinecone, or similar).
• Familiarity with RBAC, SSO/authentication, audit logging, and secure coding practices for enterprise platforms. • Exposure to document intelligence / document processing systems (parsing, OCR, formatting/validation engines) is desirable.
• Comfortable working with REST APIs, version control (Git), CI/CD pipelines, and Agile/Scrum delivery practices.
• Strong problem-solving skills, attention to detail, and ability to work effectively within a distributed delivery team.
• A2A/UI frameworks and conversational AI solutions Preferred / Good to Have
• Prior experience in legal, professional services, or document-heavy compliance-driven industries.
• Exposure to Databricks, LLM hosting/routing across multiple providers (OpenAI, Azure OpenAI, Anthropic, etc.), and LLM evaluation frameworks.
• Relevant certifications such as Microsoft Certified: Azure AI Engineer Associate. Foundational Knowledge Expected Candidates should possess a working understanding of:
• How Large Language Models (LLMs) work
• LLM architecture and key concepts
• Core Machine Learning concepts and terminology . However, we are not evaluating candidates for deep research, model training, or scientist-level expertise.

What We Are Looking For We need professionals who can quickly understand:

• What we are building
• The business problems we are solving
• The platforms and applications we are developing and supporting
• How to implement AI solutions in real-world enterprise environments The emphasis should be on practical application, engineering capabilities, solution design, and execution, rather than theoretical or academic depth.

In summary:

We are seeking a strong Applied AI Engineer profile with hands-on experience in modern GenAI and Agentic AI frameworks, capable of contributing immediately to development, delivery, and support of enterprise AI solutions.

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field (or equivalent practical experience).

Education Bachelor’s or master’s degree in computer science, Engineering, Artificial Intelligence, or a related field (or equivalent practical experience).