Senior Artificial Intelligence Engineer

Greetings!

Senior AI Engineer Agentic AI & Enterprise AI Solutions

Experience

• Total Experience: 10+ years
• Relevant AI/ML Experience: 5–6+ years
• Location: Remote

Role Overview

We are seeking a highly skilled Senior AI Engineer with strong expertise in Artificial Intelligence, Machine Learning, Generative AI, LLMs, Agentic AI, AI Agents, and Model Context Protocol (MCP) to design, develop, and scale enterprise-grade AI solutions.

The ideal candidate will be a hands-on AI practitioner with the ability to rapidly develop Proofs of Concept (POCs), translate complex business requirements into practical AI-driven solutions, and define scalable architecture and deployment strategies.

The candidate will work closely with business stakeholders, data scientists, software engineers, cloud teams, and enterprise architecture teams to deliver production-ready AI solutions.

Key Responsibilities

AI/ML Solution Development

• Design and develop end-to-end AI/ML solutions from concept and POC through production deployment.
• Translate business problems and requirements into scalable AI-driven solutions.
• Develop rapid POCs and prototypes to validate AI use cases and technologies.
• Design AI architectures supporting scalability, reliability, security, and performance.
• Select appropriate AI/ML models, frameworks, and technologies based on business requirements.
• Optimize AI applications for latency, scalability, cost, and resource efficiency.
• Define technical roadmaps for moving AI POCs into enterprise production environments.

Generative AI & LLM

• Design and implement enterprise Generative AI and LLM-based applications.
• Develop Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge and document use cases.
• Implement effective prompt engineering strategies for LLM applications.
• Work with LLMs for conversational AI, knowledge assistants, automation, and enterprise use cases.
• Evaluate and optimize LLM performance, response quality, latency, and cost.
• Experience with LLM fine-tuning and model customization is preferred.

Agentic AI & AI Agents

• Design and develop AI Agents and Agentic AI solutions for enterprise applications.
• Build single-agent and multi-agent systems with intelligent orchestration and tool usage.
• Implement agent workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or Semantic Kernel.
• Develop AI agents capable of reasoning, planning, tool calling, and executing business workflows.
• Design scalable multi-agent orchestration patterns for complex enterprise use cases.
• Implement agent memory, context management, workflow control, and guardrails.

MCP & Tool Integration

• Design and implement solutions using Model Context Protocol (MCP).
• Develop and integrate MCP-based tools and services with AI agents and LLM applications.
• Enable AI agents to securely interact with enterprise APIs, databases, applications, and external tools.
• Design reusable tool-integration patterns for enterprise AI solutions.
• Implement secure and scalable AI tool-calling architectures.

AI/ML Engineering

• Develop machine learning and deep learning solutions using modern frameworks.
• Work with NLP, conversational AI, and Computer Vision use cases where required.
• Develop Python-based AI applications, services, and APIs.
• Build data processing and AI pipelines using technologies such as Apache Spark and Airflow.
• Implement model evaluation, validation, monitoring, and lifecycle management.

MLOps & Productionization

• Implement MLOps practices across the AI/ML lifecycle.
• Develop CI/CD pipelines for AI/ML applications and model deployment.
• Implement model versioning, experiment tracking, deployment automation, and monitoring.
• Work with MLflow and other MLOps platforms.
• Establish monitoring and observability for AI applications, models, APIs, and infrastructure.
• Support model retraining, rollback, lifecycle management, and production optimization.
• Ensure AI solutions meet enterprise security, governance, reliability, and compliance requirements.

Cloud & Containerization

• Deploy and operate AI/ML workloads across AWS, Azure, and/or GCP.
• Containerize AI applications using Docker.
• Deploy and manage AI workloads using Kubernetes.
• Design scalable cloud architectures for enterprise AI workloads.
• Integrate AI applications with cloud-native services, APIs, storage, compute, and monitoring platforms.

Technical Leadership

• Provide technical leadership for AI solution design and implementation.
• Mentor junior AI/ML engineers and developers.
• Conduct technical design reviews and establish engineering best practices.
• Collaborate with product managers, business stakeholders, data scientists, developers, DevOps, cloud, and security teams.
• Communicate complex AI concepts and technical solutions effectively to both technical and non-technical stakeholders.