Job description

Key Responsibilities: - AI Solution Deployment: Deploy, configure, and fine-tune AI models, algorithms, and platforms to meet client-specific use cases, ensuring seamless integration with existing systems. - Model Development & Optimization: Build, train, and optimize machine learning models (e.g., LLMs, computer vision, predictive analytics) tailored to client data and goals. - Data Pipeline Engineering: Design and implement robust data pipelines to process and transform structured and unstructured data for AI model training and inference. - Technical Problem-Solving: Diagnose and resolve complex AI-related issues, such as model performance bottlenecks or data quality challenges, often under tight deadlines. - Client Collaboration: Partner with clients to understand their business objectives, data challenges, and operational needs. Translate these into AI-driven technical requirements. - Feedback Loop: Collaborate with internal AI research and engineering teams to relay client feedback, driving improvements to models and platforms. - AI Innovation:Identify opportunities to enhance client outcomes through advanced AI techniques, such as reinforcement learning, generative AI, or real-time inference. Qualifications: - Education: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field. - Technical Skills: - Proficiency in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). - Experience with large language models (LLMs), computer vision, or other advanced AI techniques. - Strong knowledge of data engineering (SQL/NoSQL, ETL pipelines, data lakes). - Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tools (e.g., Kubeflow, MLflow). - Experience with APIs, microservices, and deploying AI models at scale. - Problem-Solving: Ability to tackle ambiguous AI challenges and deliver practical, high-impact solutions. - Communication: Exceptional ability to explain complex AI concepts and model outputs to non-technical stakeholders. - Adaptability: Thrives in fast-paced, client-facing environments with evolving requirements. - Travel: Willingness to travel to client sites as needed (up to [X]% of the time, depending on role requirements). - Experience: 4+ years in AI engineering, machine learning, or client-facing technical roles. Experience deploying AI solutions in production is a plus. Preferred Qualifications: - Experience in industries like healthcare, finance, defense, or logistics, where AI drives decision-making. - Familiarity with generative AI, reinforcement learning, or real-time AI inference systems. - Prior experience in consulting or deploying AI solutions in a SaaS/enterprise environment. - Knowledge of DevOps practices for AI (e.g., CI/CD for ML models, containerization with Docker/Kubernetes).

Requirements