Senior AI Engineer
About the Role
As a Senior AI Engineer in a services organization, you will work on client-facing AI initiatives ranging from LLM integrations and RAG systems to predictive analytics and computer vision models. You will not only build models but also translate business problems into practical AI solutions that can operate reliably in production.
You will collaborate with backend developers, data engineers, DevOps teams, and client stakeholders. This role requires strong technical depth, structured experimentation, and the ability to move AI systems from concept to deployment.
Responsibilities
You will analyze client use cases and determine whether AI is appropriate, what type of model is suitable, and what data strategy is required. You will design model architectures, select frameworks, and define evaluation metrics aligned with business objectives.
You will build and fine-tune machine learning and deep learning models, including LLM-based applications when required. For enterprise knowledge systems, you will design and implement retrieval-based architectures, including vector search strategies and prompt structuring.
You will work closely with data engineers to ensure training data is clean, structured, and scalable. You will also collaborate with backend teams to expose AI capabilities through secure APIs.
When deploying models, you will optimize inference performance, manage latency expectations, and ensure cost control in cloud environments. You will continuously monitor model accuracy and recommend retraining or improvement strategies.
You may also participate in pre-sales technical discussions, helping explain feasibility, effort estimation, and technical trade-offs to clients.
Requirements
6+ years of experience in AI/ML engineering
Strong proficiency in Python
Experience with PyTorch or TensorFlow
Experience building and deploying production ML systems
Hands-on experience with LLM integration or NLP pipelines
Experience working in cloud environments (AWS/Azure/GCP)
Strong understanding of model evaluation and performance metrics
Nice to Have
Experience implementing RAG-based architectures
Experience with vector databases (FAISS, Pinecone, Weaviate)
Exposure to computer vision or speech processing systems
Experience handling multi-tenant AI solutions
Client-facing communication experience
What We Offer
Opportunity to work on diverse AI projects across industries
Exposure to international clients and complex enterprise systems
A technically strong environment focused on innovation
Career growth toward AI Architect or AI Practice Lead
Competitive compensation and performance incentives
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