Senior AI/ML Engineer - AI Systems & Applied Intelligence

Devsinc is hiring a highly skilled Senior AI Engineer with 4–6 years of experience in designing, building, and deploying production-grade AI systems. The ideal candidate combines strong machine learning fundamentals with hands-on expertise in Large Language Models (LLMs), RAG architectures, and scalable ML infrastructure.
This role requires ownership of the end-to-end AI lifecycle from research and experimentation to deployment, optimization, and monitoring, while contributing to architectural decisions, mentoring engineers, and delivering applied intelligence solutions that create measurable business impact.

Responsibilities

• Design, develop, and deploy AI/ML and LLM-based models to solve real-world business problems.
• Build scalable training, fine-tuning, evaluation, and inference pipelines for production-ready AI systems.
• Design and implement RAG pipelines, embedding systems, and retrieval-based architectures.
• Optimize model performance through experimentation, structured evaluation, hyperparameter tuning, and advanced optimization techniques (quantization, batching).
• Develop APIs, microservices, and real-time inference services to expose AI capabilities in production environments.
• Implement and manage MLOps workflows including experiment tracking, model versioning, CI/CD integration, monitoring, and lifecycle management.
• Contribute to system architecture discussions, ensuring scalability, reliability, security, and performance.
• Deploy AI systems on cloud platforms (AWS, Azure, GCP) with cost and performance optimization considerations.
• Research emerging AI technologies such as LLMs, multimodal AI, and vector search, and evaluate their practical applicability.
• Mentor junior engineers and promote best practices in AI engineering and MLOps.
• Document technical designs, workflows, experiments, and project outcomes for internal knowledge sharing.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 4–6 years of professional experience in AI/ML engineering roles.
• Strong proficiency in Python with hands-on experience in PyTorch and/or TensorFlow.
• Solid understanding of machine learning algorithms, neural networks, NLP, computer vision, feature engineering, and model optimization.
• Hands-on experience with Large Language Models (LLMs), RAG pipelines, embeddings, vector databases, and fine-tuning techniques (LoRA, PEFT) or advanced prompt engineering.
• Experience deploying AI models in production environments (APIs, microservices, real-time inference systems).
• Experience implementing MLOps practices using tools such as MLflow, SageMaker, Vertex AI, Weights & Biases, Docker, Kubernetes, and CI/CD pipelines.
• Hands-on experience with cloud platforms (AWS, Google Cloud) for AI solution deployment.
• Understanding of distributed systems, GPU acceleration, and scalable ML infrastructure is a plus.
• Leadership & Growth-Oriented: Capable of guiding teams, owning technical direction, and continuously learning and adapting to emerging AI technologies.
• Excellent Communication: Strong verbal and written communication skills, with the ability to effectively engage in client-facing roles and cross-functional collaboration.