Senior AI/ML Engineer
Role details
Job location
Tech stack
Job description
We are looking for a highly skilled Senior AI/ML Engineer to join our AI/ML engineering team. The ideal candidate will be responsible for developing AI & ML models and GenAI solutions that power real production use cases. The environment is fast-paced and high-responsibility, with plenty of room to grow alongside a technical, mission-driven team., AI & ML Model Development & Implementation:
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Build, train and optimize ML models, including defining preprocessing, feature engineering, validation strategies, and hyperparameter tuning
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Design, develop and optimize deep learning and GenAI models, including training and fine-tuning for various applications
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Develop AI solutions using LLMs, agentic AI frameworks, RAG pipelines, MCP servers and other related technologies
AI & ML Engineering:
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Build data ingestion and transformation pipelines to support model training and deployment and ensure reproducible, scalable models
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Verify data quality, perform data cleaning, and oversee data acquisition processes as needed.
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Develop scalable training and inference pipelines for AI-powered applications
Deployment & Integration
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Deploy models to production and create APIs to integrate AI models into business operations
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Build and drive adoption of emerging LLM/MLOPs technologies, integrating AI Agents, RAGs, and LLMs using MCP workflows to streamline automation, performance tuning, and large-scale data insights
Research & Innovation:
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Continously research and integrate the latest advancements in AI, ML and LLMs into products
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Translate scientific insights into working prototypes that bridge research and production-ready implementation
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Collaborate with internal and external research partners, including academic institutions, industry labs, and vendors
Requirements
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4+ years of experience in ML engineering, Applied AI or Research engineering
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Proven hands on experience in developing machine learning models
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Hands-on expertise with GenAI technologies and concepts like LLMs, RAG, Vector databases, AI agents, MCP, LoRA, QLoRA
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Strong Python skills and experience with ML frameworks (e.g., PyTorch, TensorFlow, sklearn), with a proven track record of building and deploying ML models
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Practical experience deploying and scaling ML models and agentic AI applications in a production environment
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Knowledge of LLM/MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes)
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Strong problem-solving skills with excellent communication and documentation skills
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Ability to work collaboratively with cross-functional teams
Nice to have Skills:
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Experience building multi-hop RAG systems with self-consistency and chain-of-thought prompting
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Experience with LLM inference and serving (vLLm, LiteLLM, TensorRT-LLM)
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Awareness of LLM-specific risk domains, including hallucinations, fairness, and data privacy