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Design and implement robust, scalable AI/ML solutions, leveraging LLMs, RAG pipelines, fine-tuning, and prompt engineering.

Build and integrate AI pipelines for document intelligence, NLP, and reasoning tasks using cloud-native tools and frameworks.

Collaborate with architects and engineers to embed AI across core platform workflows, ensuring performance, scalability, and maintainability.

Contribute to model experimentation, validation, and deployment pipelines with strong emphasis on MLOps and GenAIOps practices.

Mentor fellow engineers on AI engineering principles and establish coding and design standards across AI modules.

Work with the R&D leadership to align technical initiatives with the overall product roadmap and release strategy.

5+ years of hands-on experience in product-based tech companies, with a focus on AI/ML systems.
Proven track record in designing and implementing GenAI-powered solutions using LLMs and transformer-based architectures.
Deep understanding of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and LLM-based workflows.
Strong experience with Azure Cognitive Services.
Proficiency in Python or other relevant languages (e.g., TypeScript, Go), including use of AI/ML frameworks (e.g., LangChain, Hugging Face, PyTorch).
Familiarity with containerized environments and orchestration tools (e.g., Docker, Kubernetes).
Comfortable working with multi-cloud environments (Azure, AWS, or GCP).
Experience with Agile/Scrum development methodologies.
Strong communication skills and fluency in English (spoken and written).

Experience with building high-performance distributed systems.
Familiarity with agentic frameworks for multi-step reasoning or workflow automation.
Exposure to AI model governance, data compliance, and MLOps pipelines.
Background in Computer Science or related technical degree.

PythonTypeScriptGoAWSAzureGCPKubernetesDockerPyTorchLLM