We're looking for AI/ML Engineers!
Responsibilities
- Support development of Python-led AI/ML components, scripts and AI pipeline utilities under guidance from senior engineers.
- Assist in prompt engineering, structured output testing, basic RAG implementation, and validation of LLM responses.
- Participate in API testing, integration validation, documentation, and defect resolution activities.
- Contribute to unit testing, AI output checks, data preparation, debugging, and deployment support.
- Build foundational understanding of Agentic AI workflows, tool calling, orchestration and enterprise integration patterns.
Must Have Skills
- Python & Full-Stack Development
- Agentic AI (LangChain, LangGraph, MCP, RAG)
- Good Python programming fundamentals including scripting, data structures and Object-Oriented Programming concepts.
- Basic exposure to AI/ML concepts, GenAI, prompt engineering or LLM-enabled applications.
- Understanding of REST APIs, JSON, Git and software development lifecycle basics.
- Ability to write clean code, test outputs, document work, and learn fast in a POD-based delivery model.
- Ability to be a team player
Secondary Skills
- Exposure to RAG, vector databases, LangChain, LangGraph, Semantic Kernel or CrewAI is preferred.
- Basic understanding of cloud platforms, Docker, CI/CD, testing and observability concepts.
- Interest in responsible AI, AI guardrails, enterprise integration and production-readiness practices.
Skill Area
Skill Requirement
Addl Notes
Agentic AI Concepts
Deep understanding of AI agent design, reasoning loops, orchestration patterns & multi-agent coordination architectures
Core differentiator; senior levels lead architecture design
Agentic AI Concepts
Tool calling, function routing, agent memory & state management, autonomous decision-making patterns
Applicable across levels; depth scales with seniority
LLM & Prompt Engineering
Hands-on with LLMs (GPT-4, Claude, Gemini); prompt engineering, few-shot, chain-of-thought & structured output techniques
Focus on prompt craft
LLM & Prompt Engineering
RAG pipeline design, vector database integration (Pinecone, Weaviate, ChromaDB) & semantic search for enterprise grounding
RAG critical for enterprise-grade AI accuracy
AI Frameworks
Exposure in LangGraph, LangChain, Semantic Kernel or CrewAI for production-grade agentic workflow development
Programming & APIs
Strong Python skills — async programming, OOP, data structures & scripting for AI pipelines; Java/.NET acceptable
Python strongly preferred for AI workloads
Programming & APIs
REST/GraphQL API development, microservices design & enterprise application integration patterns
Integration Skills Essential For Enterprise Deployment
Cloud & DevOps
Azure / AWS / GCP hands-on experience; cloud-native architecture, infrastructure provisioning & managed AI services
AWS preferred for this engagement; cloud-agnostic skills valued
Cloud & DevOps
Containerization (Docker, Kubernetes), CI/CD pipeline setup, GitOps & automated deployment practices
CI/CD mandatory
Security & Responsible AI
Security principles, identity management (OAuth, Azure AD), AI guardrails, bias mitigation & enterprise compliance
Enterprise Integration
Integrating with enterprise platforms: ServiceNow, Appian, SAP, Salesforce & Microsoft ecosystem (M365, Teams, Power Platform)
Platform experience maps directly to client landscape
Testing & Observability
AI solution testing, LLM output evaluation, observability (tracing, monitoring), performance tuning & cost optimization
Observability critical for production AI agents
(*Note: Scoutit India is an independent candidate sourcing partner for this hiring. All applications, candidate screening, and hiring-related actions are conducted directly by the respective employer’s authorized recruitment team. Relevant candidates will be contacted via email and asked to fill in additional details as part of the next stage of the application process.)
Skills: integration,testing,python