AIML Engineer

Job Description – AI/ML Engineer

Role Overview

The AI/ML Engineer is responsible for designing, building, deploying, and operating production-grade machine learning and AI systems at enterprise scale. This role bridges data science, software engineering, and platform engineering to ensure AI models and applications are reliable, secure, observable, and governed throughout their lifecycle.

Must-Have Requirements

The following skills and experience are essential for this role:

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  Proprietary LLM Integration: Hands-on experience integrating at least one proprietary large language model (e.g., OpenAI ChatGPT, Anthropic Claude, or Google Gemini) directly within application code.

  
  


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  Python: Strong coding experience in Python, including production-quality software development practices.

  
  


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  LLM Orchestration Frameworks: Experience with at least one LLM application framework such as LangChain, LlamaIndex, or Haystack for building LLM-powered pipelines and applications.

  
  


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  Agentic Orchestration: Experience with at least one agentic AI orchestration library such as LangGraph, AutoGen, or CrewAI for building multi-step, multi-agent workflows.

  
  


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  Natural Language Processing (NLP): Strong foundational understanding of NLP concepts, techniques, and best practices.

  
  


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  MLOps: Proven experience in machine learning operations, including model lifecycle management, CI/CD for ML, and production monitoring.

  
  

Key Responsibilities

MLOps & AI Platform Engineering

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  Design, build, and maintain end-to-end MLOps pipelines covering model development, training, validation, deployment, monitoring, retraining, and retirement.

  
  


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  Develop and operate scalable AI/ML platforms across development, test, and production environments.

  
  


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  Operationalise machine learning and LLM-based solutions with strong reliability, performance, and governance controls.

  
  


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  Support batch and real-time inference workloads.

  
  

Software Engineering & Automation

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  Develop production-quality services and automation using Python and modern software engineering practices.

  
  


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  Build and manage CI/CD pipelines using Git-based workflows.

  
  


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  Implement Infrastructure as Code (IaC) using Terraform, ARM, CloudFormation, or Bicep.

  
  

Containerisation & Cloud Deployment

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  Deploy AI workloads using Docker and Kubernetes.

  
  


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  Design cloud-native architectures on Azure, AWS, or GCP.

  
  


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  Implement blue/green, canary, and progressive deployment strategies.

  
  

LLMs & Generative AI

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  Implement LLM application patterns including prompt orchestration, Retrieval-Augmented Generation (RAG), embeddings, and vector databases.

  
  


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  Integrate with LLM platforms such as Azure OpenAI, OpenAI, Anthropic, or AWS Bedrock.

  
  


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  Work with agentic AI frameworks and orchestration tools.

  
  

Monitoring, Observability & Operations

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  Implement logging, metrics, tracing, and alerting for AI services.

  
  


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  Monitor model performance, drift, latency, and availability.

  
  


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  Provide on-call production support and incident resolution.

  
  

Security, Governance & Compliance

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  Implement IAM, secrets management, encryption, and network security controls.

  
  


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  Ensure compliance with enterprise governance and audit requirements.

  
  

Qualifications & Experience

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  5+ years of experience in MLOps, AI Engineering, or Platform Engineering.

  
  


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  Strong Python and Linux experience.

  
  


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  Demonstrated experience deploying AI systems in production environments.

  
  

Preferred Skills

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  Experience with ML platforms such as MLflow, Kubeflow, SageMaker, or Azure ML.

  
  


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  Experience designing and operating high-availability AI platforms.

  
  


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  Familiarity with ITIL or enterprise service management processes.