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Remote- AI Engineer- Offshore should be able to work in USA EST Time zone
Core Responsibilities (AI/ML, Python, AWS, GenAI)
Design and implement end-to-end AI/ML and Generative AI solutions using Python, including model training, evaluation, optimization, and deployment.
Build and maintain cloud‑native applications on AWS using services such as Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
Develop high‑performance Python microservices (FastAPI/Flask) enabling scalable data pipelines, model inference, and real‑time analytics.
Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection).
Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure‑as‑code (Terraform/CloudFormation) for reliable, automated deployments.
Build robust MLOps workflows, including model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.
Job ID: 149061295
Skills:
Grafana, Docker, AWS, Gcp, Azure, Kubernetes, Neo4j Knowledge Graphs, Agent orchestration, Agent communication, FastAPI REST APIs, Human-in-the-loop workflows, LangSmith, Agent memory, Python Advanced, LangGraph, OpenTelemetry, RAG Vector Databases, Tool calling, LangChain, Agentic AI Multi-Agent Architectures, OpenAI Anthropic Gemini APIs, CrewAI AutoGen MCP, Prompt Engineering, Failure recovery
Skills:
Python, Agentic AI systems, RAG Retrieval-Augmented Generation, ML DL model development, LLM integration, HTTP REST APIs, LLM-based applications, AI application development, prompt engineering
Skills:
containerization , Cloud Computing, Aws Services, Microservices, Web Architecture, Restful Apis, Full-stack development, Prompt engineering, Vector databases, RAG implementation, Monitoring and observability tools, Scalable and secure deployment patterns, Responsible AI practices, Cloud-native architectures, Automated workflow design and implementation, Advanced Python programming, LLM capabilities, Fine tuning, Data engineering principles, Generative AI models, RAG architectures
Skills:
Tensorflow, Machine Learning, Jax, Pytorch, MLops, Python, Computer Vision, Deep Learning, Generative AI, AI workflow orchestration frameworks, Transformer-based architectures
Skills:
Tensorflow, Hadoop, Pytorch, Gcp, Spark, Azure, Python, AWS, Scikit-learn
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