AWS AI/ML Engineer

Job Description: Senior AWS Solutions/AI Architect (Agentic AI & Cloud Solutions)

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• Lead architecture design for agentic AI systems leveraging AWS services (Bedrock, SageMaker, Lambda, Step Functions, EventBridge, OpenSearch, DynamoDB).
• Define multi-agent orchestration strategies for clinical data workflows, source verification, and discrepancy resolution.
• Evaluate trade-offs between LLM-based reasoning, rules engines, and hybrid AI architectures.
• Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred
• Mentor AI engineers, developers, and DevOps teams on agent-based architectures.
• Collaborate with clinical SMEs, compliance teams, and sponsors to align technical design with business outcomes.

• Bachelors/Masters in Computer Science, AI, or related field (PhD a plus).
• 8–12 years in AI/ML engineering with 3–5 years in cloud-native AI architecture.
• Hands-on experience with AWS AI stack (Bedrock, SageMaker, Comprehend Medical, Textract, Kendra, Lex).
• Strong understanding of agentic AI frameworks (LangChain, LlamaIndex, Haystack, CrewAI, AutoGen).
• Proven expertise in workflow orchestration (Step Functions, Airflow, Temporal) and multi-agent pipelines.
• Experience in healthcare/Life Sciences AI solutions with regulatory compliance.
• Strong leadership, stakeholder communication, and solution governance skills.

Job Description: AWS AI Engineer / Developer (Agentic AI Implementation)

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• 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.

• 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.

Job Description: Machine Learning Engineer (Agentic AI – AWS) -

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).