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AWS AI/ML Engineer

2 weeks ago

Cochin, Kerala, India Inforizon Full time ₹ 15,00,000 - ₹ 20,00,000 per year

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

======================================================

Responsibilities
  • 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.
Qualifications
  • 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)

========================================================

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.

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).