Senior AI/ML Engineer - AI Systems & Applied Intelligence at Devsinc

Lahore, Punjab, Pakistan -

Full Time

Start Date

Immediate

Expiry Date

23 May, 26

Salary

0.0

Posted On

22 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, TensorFlow, LLMs, RAG Architectures, MLOps, Deployment, Optimization, APIs, Microservices, Cloud Platforms, Vector Search, Prompt Engineering, Quantization, CI/CD, Docker

Industry

IT Services and IT Consulting

Description

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.

Responsibilities

The role involves designing, developing, and deploying AI/ML and LLM-based models to solve business problems, including building scalable pipelines for training, fine-tuning, and inference. Responsibilities also cover implementing MLOps workflows, contributing to system architecture, and mentoring junior engineers.