Requirements

- Strong experience with FastAPI (or equivalent async frameworks), including dependency injection, UV, Pydantic, and async/await patterns (including thread pool executors for blocking operations). - Solid understanding of REST API design, including multi-tenancy, pagination, filtering, JWT/OAuth2 authentication, and structured error handling. - Proficiency in SQLAlchemy (including async sessions), raw parameterized queries, schema design, and migrations. - Hands-on experience integrating multiple LLM providers (e.g., OpenAI, Anthropic, AWS Bedrock, Ollama, Google Gemini, Snowflake Cortex) using provider abstraction layers. - Experience with JSON response validation, markdown/code-block extraction, and fallback error handling (preferably using frameworks like Pydantic). - Knowledge of prompt engineering techniques, including context injection, temperature/token tuning, and confidence scoring. - Familiarity with embedding-based retrieval and similarity scoring. - Experience with production-grade agentic frameworks such as Pydantic AI (structured output generation, agents). - Strong experience with gradient boosting models (e.g., XGBoost, LightGBM), including GPU-accelerated training, hyperparameter tuning, and evaluation. - Expertise in segmentation, anomaly detection, and feature engineering on high-frequency sensor data. - Experience with train/test splits, feature engineering, model evaluation (R², MAE, etc.), and experiment tracking (e.g., MLflow). - Understanding of when to combine classical ML with LLM-based components (e.g., LLM-assisted labeling, embedding features in tree models). - Strong database knowledge, including complex schemas, JSONB, partitioned tables, row-level security, query optimization, and vector extensions (e.g., pgvector). - Familiarity with NoSQL databases like MongoDB and specialized databases such as Redis and Qdrant is a plus. - Experience with Snowflake (including Snowpark, Model Registry, and Cortex) or equivalent platforms. - Hands-on experience with AWS services such as Bedrock, ECS, and EC2. - Experience with Docker and CI/CD pipelines. - Familiarity with S3 or equivalent object storage solutions. - Ability to work within VPN-gated infrastructure. - Experience across multiple client environments or industries (consulting background preferred). - Exposure to Industrial IoT or sensor data (high-frequency telemetry, signal processing). - Experience in NL-to-SQL or text-to-query system design. - Ability to handle multilingual data and implement internationalization.

Responsibilities

• Design and integrate LLM-powered features, including conversational interfaces, AI agents, structured generation, and retrieval-augmented systems.
• Build and maintain ML pipelines for prediction, anomaly detection, classification, and time-series analysis.
• Develop backend APIs and services connecting data sources, models, and client-facing applications.
• Work with structured and unstructured data across relational databases, data warehouses, and external APIs.
• Optimize model performance and scalability for production environments, including monitoring and fine-tuning.
• Collaborate with cross-functional teams (product, data, and engineering) to translate business requirements into technical solutions.
• Ensure code quality, documentation, and best practices for deployment, testing, and maintainability.