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.