AI/ML Engineer

Job Description

AI/ML Engineer

The ideal candidate combines deep technical expertise in AI/ML with strong solution design, stakeholder management, and hands-on implementation experience.

Key Responsibilities

• Design and architect

  end-to-end Generative AI solutions

  (text, image, speech, code, etc.) that meet business objectives and technical requirements.
• Define scalable architectures for

  LLM integration

  ,

  prompt orchestration

  ,

  retrieval-augmented generation (RAG)

  , and

  multi-agent workflows

  .
• Evaluate and select appropriate AI models, frameworks, and cloud services (e.g., OpenAI, Anthropic, Hugging Face, Azure OpenAI, Vertex AI).
• Develop architecture blueprints, data flows, and API integration strategies.
• Provide

  technical direction

  and mentorship to development teams on GenAI solution design and deployment best practices.
• Partner with

  Data Scientists, ML Engineers, and MLOps teams

  to operationalize and monitor generative AI models.
• Guide the implementation of

  responsible AI practices

  , including bias detection, data governance, and model explainability.
• Stay ahead of emerging GenAI technologies, frameworks, and model capabilities.
• Prototype and evaluate new approaches (e.g.,

  RAG

  ,

  fine-tuning

  ,

  LoRA

  ,

  function calling

  ,

  multi-agent systems

  ) to accelerate solution development.
• Drive

  proof-of-concepts (PoCs)

  and

  pilot initiatives

  to demonstrate business value.
• Collaborate with

  business leaders, product managers, and clients

  to identify use cases, gather requirements, and define success metrics.
• Translate business challenges into AI-powered solutions with measurable outcomes.
• Present architecture designs and recommendations to technical and executive audiences.

Required Skills & Experience

• 4+ years

  of experience in software architecture, solution design, or AI/ML systems development.

• 2+ years

  specifically in

  Generative AI, LLM-based solutions

  , or

  AI product architecture

  .
• Strong hands-on experience with:

• OpenAI API, LangChain, LlamaIndex, Hugging Face,

  or similar frameworks

• Vector databases

  (e.g., Pinecone, Weaviate, FAISS, Milvus)

• Cloud AI platforms:

  Azure AI, AWS Bedrock, Google Vertex AI

• Python, FastAPI, RESTful APIs, microservices,

  and

  MLOps pipelines

• Deep understanding of

  LLM lifecycle management

  ,

  prompt engineering

  ,

  model evaluation

  , and

  data pipelines

  .
• Familiarity with

  enterprise AI architecture patterns

  — security, scalability, monitoring, and governance.
• Strong presentation and stakeholder engagement skills.