Strong experience with FastAPI (or equivalent async frameworks), including dependency injection, UV, Pydantic, and async/await patterns (including thread pool executors for blocking operations)
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 production-grade agentic frameworks such as Pydantic AI (structured output generation, agents)
Requirements:
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
Optimize model performance and scalability for production environments, including monitoring and fine-tuning
Job description
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.
