Senior Python / AI Engineer Role Overview We are looking for a Senior Python/AI Engineer with strong experience in machine learning, data engineering, cloud-based architectures, and AI agent pipelines. You will work directly with the system architect to design and build: AI-powered data pipelines, prediction and scoring models, market-resolution oracles, agentic processing pipelines (AWS Bedrock / LangChain / custom), high-performance AWS-backed microservices and event-driven workflows. This role is suited for a self-driven engineer who can operate in a fast-moving architecture.

Key Responsibilities AI / ML Develop and train ML models for market signals, behavioral patterns, user risk segmentation, anomaly detection. Implement embedding pipelines, vector search and semantic analysis using: AWS Bedrock (Titan, Claude), SageMaker, LangChain, FAISS, OpenSearch, or local pipelines. Build LLM-based agents using LangGraph, LangChain, AWS Bedrock Agents, or custom orchestration. Work with HuggingFace, PyTorch, scikit-learn, Transformers, Nomic embeddings, etc.

Python Engineering Design clean, modular services for data collection, processing, analytics and agentic workflows. Build real-time pipelines using: asyncio, WebSockets, FastAPI, Redis Streams, Kafka, Celery, Apache Beam (optional). Implement microservices interacting with internal APIs, AWS services and data layers. Write production-quality Python (3.10+) with Pydantic, SQLAlchemy, Poetry/pipenv, type checking (mypy), and tests (pytest).

Data Engineering Create ETL/ELT pipelines aggregating both on-chain and off-chain datasets using: AWS Glue, AWS Lambda, Step Functions, Athena, S3, DynamoDB Streams, Kinesis. Optimize storage and data access: PostgreSQL, DynamoDB, Redis, S3, OpenSearch. Implement observability and monitoring: CloudWatch Logs, Metrics, X-Ray, OpenTelemetry.

DevOps / Cloud (nice to have) Experience with AWS:Lambda (Python runtime) ECS Fargate Bedrock (LLMs, embeddings, agents) SageMaker (model training & deployment) SQS, SNS, EventBridge API Gateway OpenSearch Neptune (graph DB) KMS, IAM best practices

Build and monitor ML services in production using: SageMaker endpoints, CI/CD, Docker, Terraform, GitLab CI.

Requirements Must-Have 5+ years of Python engineering experience. Strong background in AI/ML, especially NLP and agent-based architectures. Experience with LLMs, embeddings, RAG, and vector DBs (FAISS, OpenSearch, Pinecone). Strong understanding of async Python and distributed systems. Experience with data pipelines (ETL/ELT), real-time event-driven processing. Ability to work independently and architect solutions end-to-end. Familiarity with AWS cloud services (at least S3, Lambda, API Gateway, CloudWatch).

Nice-to-Have Experience with blockchain (EVM, Polygon, oracles). Experience with AWS SageMaker training pipelines. Understanding of smart-contract-driven workflows. Experience with graph analytics: Neo4j, AWS Neptune, RDF/Gremlin. Basic Solidity understanding. Experience with agent frameworks such as LangGraph.