Senior Software Engineer developing embedded software platforms for Infotainment and Software Defined Vehicle solutions at GM. Leading design and integration efforts for Android/QNX/Embedded Linux systems.
About the role
- ML/AI Specialist translating business problems into data-driven solutions and building end-to-end ML systems. Collaborating with diverse teams to ensure production ownership and maintain model performance.
Responsibilities
- Translate business problems into ML/AI solutions and measurable success criteria
- Build reliable data pipelines (batch/stream) for training and inference; implement data validation and quality checks
- Develop, train, and evaluate models (classical ML and deep learning) with reproducible experiments
- Design and ship production services (APIs, batch jobs, streaming consumers) with automated tests and observability
- Establish and maintain MLOps foundations: versioning (code/data/models), experiment tracking, model registry, CI/CD, and automated deployments
- Monitor production systems (latency, throughput, cost, model performance, drift) and implement retraining/rollbacks
- Apply modern AI techniques: LLM integrations, retrieval-augmented generation, fine-tuning/adapters, prompt design and evaluation, guardrails
- Optimize cost and performance (profiling, batching, caching, quantization, GPU utilization) and ensure reliability
- Collaborate with product, data, and engineering stakeholders; document designs and decisions
Requirements
- 3-5+ years building ML-powered products with production ownership (data - model - deployment - monitoring)
- Strong Python and software engineering fundamentals: clean code, testing, logging, type hints, code reviews, modular design
- Proficiency with ML/DL stack: scikit-learn; PyTorch or TensorFlow; pandas/NumPy; solid grasp of evaluation metrics and experiment design
- SQL and data modeling; experience with warehouses/lakehouses (e.g., BigQuery/Snowflake/Redshift) and ETL/ELT tools
- Orchestration and pipelines: Airflow/Prefect/Dagster or similar
- Containers and deployment: Docker; basic Kubernetes or serverless; API frameworks (FastAPI/Flask)
- Cloud experience (AWS/GCP/Azure) including storage, compute, networking, and IAM basics
- MLOps tooling: experiment tracking and model management (MLflow, Weights & Biases), model registry, artifact/version control
- Monitoring/observability: metrics, tracing, and alerting (Prometheus/Grafana/CloudWatch/Datadog); model drift monitoring
- Practical AI/LLM experience: using hosted APIs or open-source models, embeddings/vector databases (FAISS/Pinecone/pgvector), RAG patterns, safety/guardrails
- Clear communication and the ability to scope, estimate, and deliver incrementally
- BS/MS in Computer Science, Data Science, Statistics, Engineering, or equivalent practical experience
- English Proficiency - B2+
Benefits
- Flexible working time - you can agree on it within the team
- Necessary tools and equipment
- Communication in English - only foreign customers, and international Teams
- Simple structure and 'open door' way of communication
- Full-time English teachers
- Medical insurance for employees
- HiQo University- internal education and training programs
- HIQO COINS - We have a system of rewarding employees for extracurricular activities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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