We are seeking a Senior AI/ML Engineer to design and build scalable LLM-powered systems and Agentic AI platforms. This role involves architecting multi-agent workflows, production-grade AI infrastructure, and advanced machine learning solutions that automate complex business processes and power intelligent applications.
The ideal candidate combines deep machine learning expertise, strong software engineering practices, and hands-on experience building LLM-based and agent-driven systems in production environments.
Key Responsibilities:
AI/ML & LLM Development
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Design and develop end-to-end AI/ML solutions including data pipelines, model training, deployment, and monitoring.
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Build LLM-powered applications using transformer models (GPT, BERT, LLaMA, Mistral).
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Implement Retrieval-Augmented Generation (RAG) systems with embeddings, semantic search, and vector databases.
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Optimize models through fine-tuning, prompt engineering, quantization, and inference optimization.
Agentic AI Systems
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Design and implement multi-agent architecture using frameworks such as LangGraph, CrewAI, Haystack, AutoGen, or custom orchestration layers.
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Build tool-calling workflows, API integrations, and autonomous decision pipelines.
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Implement agent reasoning, planning, memory management, and evaluation frameworks.
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Develop LLM guardrails, safety checks, and performance evaluation pipelines.
AI Platform & MLOps
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Build production-grade AI systems with monitoring, observability, and reliability.
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Develop MLOps pipelines for model training, versioning, deployment, and lifecycle management.
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Implement CI/CD pipelines, experiment tracking, and automated testing for ML workflows.
Cloud & Infrastructure
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Deploy AI systems on AWS using services such as SageMaker, Bedrock, Lambda, Step Functions, API Gateway.
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Work with data services including S3, DynamoDB, Aurora, OpenSearch, Glue, and Athena.
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Manage containerized environments using Docker, Kubernetes, ECS/EKS and IaC tools such as Terraform or CDK.
Team Leadership & Collaboration
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Lead, mentor, and grow a team of ML engineers, data scientists, and research engineers.
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Conduct code reviews, provide technical guidance, and promote best practices in ML development, AI architecture, and MLOps.
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Guide the team in building scalable AI agent systems and LLM-based applications.
Stakeholder & Project Management
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Work with leadership and cross-functional teams to define AI strategy, roadmap, and key success metrics.
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Translate business needs into actionable technical plans and ensure timely, high-quality execution.
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Create architectural documents, technical proposals, and progress reports for stakeholders.
Required Qualifications
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5–8 years of experience in AI/ML engineering or applied machine learning.
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Strong expertise in machine learning, deep learning, and transformer architectures.
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Hands-on experience with LLMs, embedding’s, prompt engineering, and RAG systems.
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Experience building AI agents, autonomous workflows, or tool-augmented LLM applications.
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Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Hugging Face.
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Experience building production ML systems and cloud-based AI platforms.
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Excellent problem-solving, system design, and communication skills.
Added Advantage
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Certifications in AI/ML, cloud computing, or data engineering.
