What are the responsibilities and job description for the Lead AI Engineer position at CoSourcing Partners?
Title: Lead AI Engineer
Location: Remote
Employment Type: Contract / Contract-to-Hire - We are open to both.
Role Summary:
We are hiring a Lead AI Engineer to anchor and grow our AI practice, with strong hands on expertise in Generative AI and traditional Machine Learning. This role combines technical leadership, solution architecture, and delivery ownership across client and internal AI initiatives.
The ideal candidate is deeply hands-on, production-focused, and capable of translating business problems into scalable AI solutions.
Key Responsibilities:
Technical Leadership:
Generative AI Delivery:
Traditional ML & Applied AI:
Production & MLOps:
Practice Growth & Client Engagement:
Required Skills:
Strong experience with modern GenAI design patterns including advanced RAG (hybrid search, reranking), agentic orchestration (LangGraph or cloud-native ADK/Strands), and parameter-efficient fine-tuning (LoRA/QLoRA).
Experience with LLM evaluation, guardrails, and model observability in production environments.
Solid understanding of ML algorithms and deep learning.
Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.
Experience architecting and deploying AI solutions on AWS, GCP, or Azure.
Knowledge of containerization (Docker) and orchestration (Kubernetes).
Familiarity with ML lifecycle management tools (e.g., MLflow, Airflow).
Experience:
Location: Remote
Employment Type: Contract / Contract-to-Hire - We are open to both.
Role Summary:
We are hiring a Lead AI Engineer to anchor and grow our AI practice, with strong hands on expertise in Generative AI and traditional Machine Learning. This role combines technical leadership, solution architecture, and delivery ownership across client and internal AI initiatives.
The ideal candidate is deeply hands-on, production-focused, and capable of translating business problems into scalable AI solutions.
Key Responsibilities:
Technical Leadership:
- Lead design and development of AI solutions across:
- Generative AI (LLMs, RAG, embeddings, chatbots)
- Traditional ML (classification, regression, NLP, forecasting, etc.)
- Own end-to-end lifecycle: problem framing → model development → deployment → monitoring.
- Define best practices for model development, evaluation, and production readiness.
- Mentor and guide junior ML engineers and data scientists.
Generative AI Delivery:
- Design and implement:
- RAG-based systems
- Enterprise chatbots & copilots
- Document intelligence solutions
- Build embedding pipelines and integrate vector databases.
- Apply prompt engineering and fine-tuning where required.
- Implement guardrails, evaluation metrics, and quality controls.
Traditional ML & Applied AI:
- Develop and deploy ML models using standard frameworks.
- Optimize model performance, scalability, and reliability.
- Work with structured and unstructured data pipelines.
- Ensure models are production-ready and measurable.
Production & MLOps:
- Implement CI/CD practices for ML workflows.
- Containerize and deploy models using modern tooling.
- Set up monitoring, retraining, and drift detection.
- Collaborate with DevOps and cloud teams for scalable deployments.
Practice Growth & Client Engagement:
- Support pre-sales with solution design and effort estimation.
- Drive POCs and accelerators in GenAI and ML.
- Contribute to reusable frameworks and AI assets.
- Stay updated with evolving AI/LLM ecosystem.
Required Skills:
Strong experience with modern GenAI design patterns including advanced RAG (hybrid search, reranking), agentic orchestration (LangGraph or cloud-native ADK/Strands), and parameter-efficient fine-tuning (LoRA/QLoRA).
Experience with LLM evaluation, guardrails, and model observability in production environments.
Solid understanding of ML algorithms and deep learning.
Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.
Experience architecting and deploying AI solutions on AWS, GCP, or Azure.
Knowledge of containerization (Docker) and orchestration (Kubernetes).
Familiarity with ML lifecycle management tools (e.g., MLflow, Airflow).
Experience:
- 6–10 years in AI/ML engineering.
- 3 years leading AI/ML initiatives or mentoring teams.
- Proven track record of deploying ML or GenAI systems to production.