Senior AI/ML Engineer

📋 Description

• Oversee the design and execution of scalable machine learning systems, encompassing supervised, unsupervised, and LLM-based solutions.

• Convert research and prototypes into systems ready for production.

• Collaborate with stakeholders to pinpoint high-impact AI/ML opportunities and establish the best technical strategies.

• Provide technical guidance and support team development efforts.

• Construct and manage LLM pipelines, including prompt design, fine-tuning, and assessment.

• Create RAG-based systems utilizing embeddings, vector stores, and retrieval methods.

• Develop evaluation frameworks, feedback mechanisms, and datasets for ongoing model performance enhancement.

• Design reusable tools to expedite experimentation, deployment, and monitoring processes.

• Manage the complete ML lifecycle: data pipelines, training, deployment, monitoring, and iteration.

• Set best practices for reproducibility, observability, CI/CD, and model versioning.

• Collaborate with platform and DevOps teams to guarantee system reliability and scalability.

• Advocate for responsible AI practices, focusing on governance, fairness, and transparency.

• Lead cross-functional projects spanning data engineering, analytics, and AI/ML.

• Simplify complex ML concepts into understandable recommendations for both technical and non-technical audiences.

• Work alongside clients and internal teams to strategize and implement AI/ML solutions.

• Contribute to documentation, frameworks, and shared best practices.

• Define and lead intricate AI/ML projects that align with business objectives.

• Coordinate stakeholders and drive execution across various teams.

• Establish clear success metrics and ensure the delivery of impactful solutions.

⛳️ Requirements

• 5–7 years of experience in ML engineering, AI engineering, or related domains, with a proven track record in production deployment.

• Strong programming expertise in Python and SQL; familiarity with PyTorch and HuggingFace.

• Experience in developing LLM applications, including RAG, embeddings, and vector search techniques.

• Proficient with cloud platforms (AWS or Azure; e.g., SageMaker, Bedrock, Azure ML).

• Solid understanding of ML fundamentals: data design, training, evaluation, and experimentation processes.

• Knowledge of LLM alignment methodologies (e.g., SFT, DPO, RL).

• Familiarity with MLOps practices: CI/CD, monitoring, retraining, and experiment tracking.

• Proficient in working with complex, multi-source datasets and formulating evaluation strategies.

• Strong software engineering principles (testing, modularity, code review).

• Experience in mentoring engineers and shaping technical direction.

• Excellent communication skills for interacting with both technical and non-technical stakeholders.

🏝️ Benefits

• Competitive pay

• Excellent benefits