Experience
5 - 10 yrs
Job Location
Hyderabad, India
Vacancy
1
Designation
AI Engineer
Job Type
ONSITE
Job Description
Job_Description":"
Position: Senior AI Engineer
Location: Hyderabad
Industry: IT Services & Consulting
Department: Engineering \Software & QA
Employment Type: Full\-Time, Permanent
Overview:
We are looking for a Senior AI Engineer to design and deliver AI\-powered capabilities within business applications. The role focuses on building reliable reasoning pipelines, intelligent workflows, and agent\-driven systems that integrate enterprise data and automate complex processes.
Key Responsibilities
- Design AI workflows that execute multi\-step business tasks autonomously.
- Build agentic systems capable of planning, reasoning, and taking actions using tools and APIs.
- Develop multi\-agent architectures where specialized agents collaborate to complete processes.
- Implement Retrieval\-Augmented Generation (RAG) over enterprise data sources.
- Integrate LLMs via APIs and open\-source models using provider\-agnostic architecture.
- Design structured outputs, guardrails, and validation layers for reliable behavior .
- Build tool\-using AI for database querying, document processing, and API orchestration.
- Create evaluation pipelines to measure accuracy, reliability, and regression of AI outputs.
- Implement observability including tracing, latency, and cost monitoring.
- Design failure handling, fallbacks, and human\-in\-the\-loop workflows.
- Architect scalable async execution using queues and background workers.
- Own production reliability of AI features from deployment through monitoring.
- Evaluate approaches (RAG vs fine\-tuning vs workflow vs agents) and justify design decisions.
- Define AI engineering standards and best practices across projects.
- Establish evaluation benchmarks and acceptance criteria for AI features.
- Participate in technical hiring and mentoring of future AI team members.
Experience
- Minimum 5+ years software development experience with significant hands\-on work building production AI systems.
- Experience delivering end\-to\-end AI features used by real users.
- Hands\-on experience with RAG, agent workflows, or automation systems.
Technical Skills
- Strong Python programming skills.
- Experience with AI frameworks ( LangChain , LlamaIndex, DSPy , Semantic Kernel, or similar).
- Understanding of agents, tool calling, and workflow orchestration patterns.
- Experience with vector databases and retrieval systems.
- Knowledge of evaluation methodologies for AI outputs.
- Experience integrating AI systems with APIs, databases, and backend applications.
- Familiarity with containerized deployments and cloud platforms.
Good to Have
- Experience with multi\-agent coordination patterns.
- LLMOps practices (versioning, rollback, evaluation datasets).
- Working with open\-source \/local models.
- Security and safety considerations in AI systems.
- Security and safety considerations in AI systems.
- Working with open\-source \/local models.
- LLMOps practices (versioning, rollback, evaluation datasets).
- Experience with multi\-agent coordination patterns.
- Familiarity with containerized deployments and cloud platforms.
- Experience integrating AI systems with APIs, databases, and backend applications.
- Knowledge of evaluation methodologies for AI outputs.
- Experience with vector databases and retrieval systems.
- Understanding of agents, tool calling, and workflow orchestration patterns.
- Experience with AI frameworks ( LangChain , LlamaIndex, DSPy , Semantic Kernel, or similar).
- Strong Python programming skills.
- Hands\-on experience with RAG, agent workflows, or automation systems.
- Experience delivering end\-to\-end AI features used by real users.
- Minimum 5+ years software development experience with significant hands\-on work building production AI systems.
- Participate in technical hiring and mentoring of future AI team members.
- Establish evaluation benchmarks and acceptance criteria for AI features.
- Define AI engineering standards and best practices across projects.
- Evaluate approaches (RAG vs fine\-tuning vs workflow vs agents) and justify design decisions.
- Own production reliability of AI features from deployment through monitoring.
- Architect scalable async execution using queues and background workers.
- Design failure handling, fallbacks, and human\-in\-the\-loop workflows.
- Implement observability including tracing, latency, and cost monitoring.
- Create evaluation pipelines to measure accuracy, reliability, and regression of AI outputs.
- Build tool\-using AI for database querying, document processing, and API orchestration.
- Design structured outputs, guardrails, and validation layers for reliable behavior .
- Integrate LLMs via APIs and open\-source models using provider\-agnostic architecture.
- Implement Retrieval\-Augmented Generation (RAG) over enterprise data sources.
- Develop multi\-agent architectures where specialized agents collaborate to complete processes.
- Build agentic systems capable of planning, reasoning, and taking actions using tools and APIs.