AWS AI/ML Engineer / Senior Solutions Architect (Agentic AI_AWS & ML

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Responsibilities

Qualifications

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Responsibilities
----------------- Implement agentic AI workflows for clinical source verification, discrepancy detection, and intelligent query generation.- Build and integrate LLM-powered agents using AWS Bedrock + open-source frameworks (LangChain, AutoGen).- Develop event-driven pipelines with AWS Lambda, Step Functions, and EventBridge.- Optimize prompt engineering, retrieval-augmented generation (RAG), and multi-agent communication.- Integrate AI agents with external systems through secure APIs.- Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred- Collaborate with data engineers for PHI/PII-safe ingestion pipelines.- Monitor, test, and fine-tune AI workflows for accuracy, latency, and compliance.

Qualifications
----------------- Bachelor’s in Computer Science, Engineering, or related field.- 3–6 years in AI/ML engineering with hands-on LLM/agentic AI development.- Strong coding skills in Python/TypeScript and experience with LangChain, LlamaIndex, or AutoGen.- Familiarity with AWS AI services (Bedrock, SageMaker, Textract, Comprehend Medical).- Experience in API integrations and event-driven architectures.- Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred- Problem-solving mindset with ability to experiment and iterate quickly.

Key Responsibilities

• Design, develop, and deploy

  ML models

   for Agentic AI use cases.
• Work with

  AWS AI/ML ecosystem

   (SageMaker, Bedrock, Lambda, Step Functions, S3, DynamoDB, Kinesis).
• Preprocess and engineer features from structured, unstructured, and streaming data.
• Collaborate with

  data engineers

   to ensure high-quality, well-curated training datasets.
• Implement

  LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG)

   pipelines.
• Evaluate and optimize models for

  accuracy, performance, scalability, and cost-efficiency

  .
• Integrate models into production applications and APIs.
• Work with

  MLOps teams

   to automate training, testing, deployment, and monitoring workflows.
• Perform

  experimentation, A/B testing, and model validation

   to ensure reliability.
• Document experiments, pipelines, and best practices for reproducibility.

Required Skills & Qualifications

• 3–6 years

   of experience in ML engineering (adjust based on seniority).
• Strong programming skills in

  Python

   (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Solid understanding of

  ML lifecycle

   (data preprocessing, training, evaluation, deployment).
• Experience with

  AWS services

   for ML (SageMaker, Lambda, ECS/EKS, Step Functions, Bedrock).
• Familiarity with

  large language models (LLMs), NLP, and embeddings

  .
• Strong knowledge of

  APIs and microservice deployment

  .
• Experience with

  ML pipeline orchestration

   (Airflow, Kubeflow, MLflow, or similar).
• Understanding of

  data versioning, experiment tracking, and model registry

  .
• Proficiency in

  SQL/NoSQL databases

   and vector databases (Weaviate, Pinecone, FAISS).