AI Engineer

What will your typical day look like?

As an AI Engineer, you will focus on building, deploying, and maintaining advanced AI solutions that deliver tangible impact for our clients and internal teams. Working closely with data scientists, Dev Ops professionals, and software developers, you will:

• Develop and deploy agent-based Generative AI applications using Retrieval Augmented Generation (RAG) and multi-agent workflows.
• Implement scalable Gen AI solutions leveraging AWS Bedrock, Amazon EKS, and AWS Code Pipeline.
• Utilize open-source frameworks such as Lang Chain and Lang Graph to accelerate solution development and ensure code modularity.
• Operationalize APIs for Gen AI applications, deploying containerized solutions on Kubernetes (EKS).
• Evaluate and optimize application performance using observability tools and Gen AI evaluation frameworks.
• Investigate and enhance operational workflows, proactively identifying bottlenecks and opportunities for performance optimization.
• Build and deploy intelligent Agents capable of inter-agent communication to achieve complex objectives.
• While your primary focus will be on hands-on development, there are opportunities to get involved in solution design, client pitches, or project management if you are interested in expanding your skill set.

• Hands‑on experience building and deploying machine learning and Generative AI solutions, with expertise in agent‑based architecture.
• Deep knowledge of ML and AI concepts, particularly in Generative AI (e.g., LLMs, RAG, agent frameworks).
• Proven expertise with AWS AI/ML services, including AWS Bedrock, Containers, EKS, and Code Pipeline for production‑grade AI deployments.
• Strong proficiency in open‑source Gen AI frameworks such as Lang Chain, Lang Graph, or similar.
• Experience developing, evaluating, and optimizing RAG‑based solutions.
• Advanced Python programming skills for ML development, automation, and API design.
• A track record of deploying containerized applications as scalable web services within Kubernetes environments.
• The ability to analyze and optimize operational workflows, identify bottlenecks, and drive continuous improvement.
• Excellent communication skills, both written and verbal.
• Familiarity with MLOps best practices and large‑scale enterprise AI deployments.
• Prior experience in professional services, consulting, or advisory roles.
• A Bachelor’s degree or higher in Computer Science, Software Engineering, Electrical/Computer Engineering, or a related field.

Hybrid role- 2-3 days in office.