Senior AI Engineer

Job Purpose:The Senior AI Engineer will lead the design, development, and deployment of scalable artificial intelligence solutions. This role is responsible for bridging the gap between theoretical AI research and robust, production-ready applications. You will architect end-to-end AI pipelines, optimize machine learning models, implement generative AI solutions, and mentor junior team members to drive business automation and innovation.Key Responsibilities:AI Architecture & Development: Architect, build, and deploy production-grade AI/ML models, specialized NLP (Natural Language Processing) tools, and Generative AI solutions (including LLMs).Pipeline & Tooling Engineering: Design, implement, and maintain scalable data preprocessing and model training pipelines. Integrate vector databases and leverage frameworks like LangChain or LlamaIndex for advanced context management.LLMOps & Model Optimization: Manage model deployment, monitoring, and scalability. Optimize models for high performance and cost efficiency using techniques like fine-tuning, quantization, hyperparameter tuning, and caching solutions.Cross-Functional Collaboration: Partner closely with data engineers, software developers, product managers, and business stakeholders to translate complex organizational challenges into functional AI products.Risk & Security Management: Implement guardrails for responsible AI practices, ensuring data privacy, compliance, model interpretability, and robust defense against risks like model hallucinations or security vulnerabilities.Mentorship & Technical Leadership: Provide technical guidance, code reviews, and mentorship to junior AI/ML engineers, fostering a culture of continuous learning and engineering excellence.Required Skills & Qualifications:Technical Competencies:Programming Mastery: Exceptional proficiency in Python and standard software engineering practices (version control, CI/CD pipelines, unit testing).Frameworks & Libraries: Deep expertise in deep learning frameworks like PyTorch or TensorFlow, alongside standard ML libraries (Scikit-Learn, NumPy, Pandas).Generative AI Ecosystem: Proven hands-on experience with OpenAI APIs, Hugging Face, prompt engineering, fine-tuning techniques, and working with open-source LLMs (e.g., Llama, Mistral).Infrastructure & MLOps: Strong experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, or GCP). Familiarity with MLOps/LLMOps tools for tracking and monitoring models in production.Data & Databases: Proficiency in SQL/NoSQL databases and specialized vector databases (e.g., Pinecone, Milvus, Chroma, Qdrant).Behavioral & Leadership Competencies:Strong analytical, debugging, and complex problem-solving abilities.Excellent communication skills—the ability to clearly explain complex technical AI concepts to non-technical business partners.A proactive learner dedicated to staying updated on rapidly shifting AI research and emerging tools.Education & Experience Requirements:Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field.Experience: Minimum of 5+ years of hands-on experience developing and deploying machine learning or AI models in a production environment, with a demonstrable track record of shipping end-to-end AI products