Sr. AI Python Engineers
Algoleap Technologies
27 days ago
Full-time
On-site
Hyderabad, Telangana, India
Senior Python Engineer
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Responsibilities
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End -to -end design, development, and deployment of enterprise -grade AI solutions leveraging Azure AI, Google Vertex AI, or comparable cloud platforms.
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Architect and implement advanced AI systems, including agentic workflows, LLM integrations, MCP -based solutions, RAG pipelines, and scalable microservices.
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Oversee the development of Python -based applications, RESTful APIs, data processing pipelines, and complex system integrations.
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Define and uphold engineering best practices, including CI/CD automation, testing frameworks, model evaluation procedures, observability, and operational monitoring.
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Partner closely with product owners and business stakeholders to translate requirements into actionable technical designs, delivery plans, and execution roadmaps.
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Provide hands -on technical leadership, conducting code reviews, offering architectural guidance, and ensuring adherence to security, governance, and compliance standards.
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Communicate technical decisions, delivery risks, and mitigation strategies effectively to senior leadership and cross -functional teams.
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Required Skills & Experience
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LLM & Core AI
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Strong understanding of transformers (attention, tokens, context window) and LLM behavior.
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Hands -on with 2+ LLM providers (e.g., Azure OpenAI + Anthropic / open source like Llama/Qwen).
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Experience tuning decoding parameters and handling context window limits (truncation, sliding window, summarization).
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Prompting & Context Engineering
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Proven experience designing multi -layer prompts (system/policy, task, user, tools, retrieved context).
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Built context builders that select relevant history (recency + semantic) and inject tool + RAG outputs.
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Implemented context compression (conversation/memory summarization) and structured outputs (JSON/schema) with robust error handling.
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Tools, MCP & External Integrations
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Designed and implemented LLM tools/function schemas with validation, clear errors, and safe side -effects.
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Hands -on experience with MCP (Model Context Protocol): building MCP servers/tools for internal data and actions, including auth and multi -tenant isolation.
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Experience integrating REST/SQL/sandboxed execution tools and defining fallback/degradation strategies when tools fail.
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Agentic Systems, Orchestration & A2A
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Built multi -step agentic workflows: plan â tool calls â intermediate decisions â final answer.
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Practical use of agent roles (Planner / Worker / Critic / Router / Supervisor).
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Hands -on with A2A (Agent -to -Agent) collaboration where specialist agents exchange structured state.
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Experience with at least one agentic/workflow framework (e.g., LangGraph, LangChain agents, Google ADK, Orkes Conductor, Temporal) and checkpointed, resumable flows (Postgres/Redis).
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RAG & Knowledge Orchestration
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Delivered end -to -end RAG systems: ingestion â chunking â embedding â indexing â retrieval â synthesis.
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Implemented hybrid search (vector + keyword + filters) over enterprise sources (PDF, HTML, Confluence/SharePoint, SQL).
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Experience with query rewriting/expansion and grounded answers with citations, including debugging retrieval quality.
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Reasoning, Evaluation & Guardrails
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Implemented ReAct -style and tool -augmented reasoning patterns, including self -critique/second -pass flows.
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Defined task -level success metrics and built golden test flows from real logs to evaluate prompt/model/flow changes.
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Instrumented telemetry for tool errors, step counts, loops, latency, and cost (tokens, per feature/tenant).
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Implemented guardrails: prompt -injection defenses, per -tenant/per -role tool & data access, input/output filtering, PII -safe logging, and participated in red -teaming/adversarial testing.
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Model, Cost & Performance Engineering
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Experience choosing and combining small router/classifier models with large reasoning models.
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Implemented caching (LLM outputs, retrieval results) and optimized latency (parallelization, step count, time budgets).
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Built or contributed to cost/usage monitoring for LLM and agent workflows.
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Supporting Software Engineering
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Expert -level proficiency in Python, RESTful API development, microservices architecture, and containerized deployments (Kubernetes, Docker).
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Experience with API frameworks such as FastAPI, FastMCP, Flask, Django, and tools like Swagger/OpenAPI.
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Hands -on background in data engineering, including data transformation, SQL/NoSQL databases, and event -driven architectures.
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Deep understanding of DevOps and MLOps practices, including CI/CD pipelines, infrastructure -as -code, observability platforms, model/workflow monitoring, security, and automated testing.
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Proven ability to collaborate with cross -functional teams, manage project timelines, and drive technical alignment in complex engineering environments.
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Exceptional communication and presentation skills with the ability to convey complex AI concepts to both technical and non -technical audiences.
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