Artificial Intelligence Engineer

Job Title:

Role Overview

Key Responsibilities

• Design and implement

  RAG-based architectures

  leveraging AWS services, vector databases, and LLMs with advanced retrieval strategies (hybrid search, reranking, document processing pipelines).
• Develop and deploy

  agentic workflows

  using frameworks such as

  CrewAI, LangGraph, Bedrock Strands Agents

  , or similar, including multi-agent orchestration and tool-calling implementations.
• Build

  agent memory systems

  (short-term, long-term, semantic) and implement context management strategies for complex workflows.
• Build, train, and deploy models on

  AWS Bedrock, SageMaker, AWS Transcribe/Translate/Comprehend, Kendra

  , and related services.
• Implement

  guardrails, content filters, and safety mechanisms

  for LLM outputs including PII detection and bias mitigation.
• Design and maintain

  evaluation frameworks

  for agent performance, accuracy, and reliability using metrics like RAGAS and human-in-the-loop evaluation.
• Build

  observability and monitoring

  for agentic workflows including tracing, logging, and debugging using tools like CloudWatch, X-Ray, LangSmith, or similar.
• Develop

  prompt engineering strategies

  and prompt management systems for production environments.
• Work closely with architects and engineering teams to integrate AI capabilities into existing enterprise systems using APIs, event-driven patterns, and data pipelines.
• Develop

  Python-based pipelines and APIs

  for AI workflows and automation, including streaming responses and real-time inference.
• Optimize inference, cost, and performance of deployed models on AWS through A/B testing and continuous improvement.
• Evaluate new LLMs, embeddings, and agent frameworks, and recommend best-fit approaches for projects.
• Collaborate with cross-functional teams and stakeholders to design

  MVPs, prototypes, and production-scale solutions

  from business requirements.

Required Skills & Experience

• Hands-on AWS AI/ML stack

  : Bedrock, SageMaker, Transcribe, Translate, Comprehend, Kendra, Lambda, API Gateway, Step Functions, EventBridge.

• RAG implementation experience

  (at least one production project) using embeddings, vector databases, and hybrid search strategies.

• Agentic AI frameworks

  : CrewAI, LangGraph, Bedrock Strands Agents, or equivalent with experience in tool-calling/function-calling and multi-agent orchestration.
• Experience with

  agent orchestration patterns

  (ReAct, Plan-and-Execute, reflection) and autonomous agent design.
• Strong

  prompt engineering

  skills and experience with prompt management at scale.
• Knowledge of

  LLM evaluation metrics

  and agent performance monitoring.
• Strong

  Python programming

  skills with experience in ML/AI libraries (LangChain, HuggingFace, PyTorch/TensorFlow optional but good to have).
• Experience with

  vector/semantic databases

  such as OpenSearch, Weaviate, Pinecone, or equivalent.
• Understanding of

  observability tools

  for AI workflows (CloudWatch, X-Ray, LangSmith, Phoenix, or similar).
• Familiarity with

  enterprise integration patterns

  (APIs, data pipelines, event-driven architectures, security, observability).
• Experience with

  document processing pipelines

  for unstructured data (PDFs, images, etc.).
• Strong problem-solving and debugging skills in production environments.

Preferred (Good to Have)

• Exposure to

  MLOps practices

  (CI/CD for ML, model monitoring, retraining pipelines).
• Experience with

  fine-tuning or RLHF

  on SageMaker or Bedrock.
• Familiarity with

  data processing tools

  (Glue, Athena, Redshift, or similar).
• Understanding of

  cost optimization and scaling

  on AWS including inference optimization.
• Knowledge of

  governance frameworks

  for AI (model cards, bias detection, responsible AI practices).
• Prior experience in

  multi-tenant SaaS or enterprise platforms

  .
• Experience with

  context window management

  and advanced chunking strategies.
• Familiarity with

  reranking models

  and retrieval optimization techniques.

Qualifications

• Bachelors/Masters degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• 2+ years of hands-on AI/ML development with Generative AI solutions.
• Demonstrated ability to translate business requirements into technical AI solution