Senior AIML Engineer

HR POD - Hiring Talent Globally

Job Location:

Lahore - Pakistan

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

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.

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 a...

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.