Senior Python / AI Engineer in Data Pro Software Solutions

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.