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Data Scientist

Actively Reviewing the Applications

Ubique Systems

India, Telangana, Hyderabad Full-Time On-site

Posted 5 hours ago • Apply by June 9, 2026

Job Description

Role: Senior AIML Python Engineer

Experience: 6+ Years

Positions: Senior AIML Python Engineer

Exp: 6-14 Years

Location: Hyderabad

Work mode: 2 Days WFO

NP: Immediate/15 Days

Mandatory Skills: AIML, LLM, Core AI, Azure, MCP, RAG, Docker/Kubernetes

Responsibilities:

• End-to-end design, development, and deployment of enterprise-grade AI solutions leveraging Azure AI, Google Vertex AI, or comparable cloud platforms.

• Architect and implement advanced AI systems, including agentic workflows, LLM integrations, MCP-based solutions, RAG pipelines, and scalable microservices.

• Oversee the development of Python-based applications, RESTful APIs, data processing pipelines, and complex system integrations.

• Define and uphold engineering best practices, including CI/CD automation, testing frameworks, model evaluation procedures, observability, and operational monitoring.

• Partner closely with product owners and business stakeholders to translate requirements into actionable technical designs, delivery plans, and execution roadmaps.

• Provide hands-on technical leadership, conducting code reviews, offering architectural guidance, and ensuring adherence to security, governance, and compliance standards.

• Communicate technical decisions, delivery risks, and mitigation strategies effectively to senior leadership and cross-functional teams.

Required Skills & Experience:

LLM & Core AI

• Strong understanding of transformers (attention, tokens, context window) and LLM behavior.

• Hands-on with 2+ LLM providers (e.g., Azure OpenAI + Anthropic / open source like Llama/Qwen).

• Experience tuning decoding parameters and handling context window limits (truncation, sliding window, summarization).

Prompting & Context Engineering

• Proven experience designing multi-layer prompts (system/policy, task, user, tools, retrieved context).

• Built context builders that select relevant history (recency + semantic) and inject tool + RAG outputs.

• Implemented context compression (conversation/memory summarization) and structured outputs (JSON/schema) with robust error handling.

Tools, MCP & External Integrations

• Designed and implemented LLM tools/function schemas with validation, clear errors, and safe side-effects.

• Hands-on experience with MCP (Model Context Protocol): building MCP servers/tools for internal data and actions, including auth and multi-tenant isolation.

• Experience integrating REST/SQL/sandboxed execution tools and defining fallback/degradation strategies when tools fail.