- Title: L2 / L3 Engineer AI, AIOps & Intelligent Automation (LLM / Chatbot Engineering)
- Business/Function: Cloud Engineering & Infrastructure Services
- Level: L2/L3
- Location: Mumbai
- Summary: The L2 / L3 Engineer AI, AIOps & Intelligent Automation will be responsible for designing, developing, and implementing AI-driven solutions, including chatbots, Large Language Model (LLM)-based systems, and AIOps frameworks using open-source and enterprise technologies. The role focuses on enhancing IT Service Delivery, Observability, Command Center operations, and automation using AI/ML techniques, with an emphasis on proactive monitoring, anomaly detection, incident automation, and intelligent user interactions.

Additional Information

- Responsibilities:Design and develop AI/ML-based solutions for IT operations and business automation
- Build and deploy chatbots using LLMs, NLP frameworks, and conversational AI platforms (e.g., Dialogflow, Azure OpenAI, LangChain, Rasa)
- Develop and integrate GenAI/LLM-based applications for:
- ITSM automation
- Knowledge management
- Incident troubleshooting
- User query resolution

AIOps & Observability

- Implement AIOps use cases such as:
- Anomaly detection in system metrics
- Alert correlation and noise reduction
- Root cause analysis (RCA)
- Predictive failure analysis
- Integrate AI solutions with:

Prometheus, Grafana, ELK stack

- Monitoring tools and observability platforms

Automation & Integration

- Develop automation pipelines integrating:

AI models

- Power Automate / Python scripts
- APIs and microservices

Build intelligent workflows combining:

- Alerts AI processing Task/incident generation





L2 Responsibilities:

- Monitor AI models and chatbot performance
- Handle basic tuning and troubleshooting
- Resolve chatbot errors and data issues
- Support deployment and testing of AI solutions

L3 Responsibilities:

- Design and implement LLM-based architectures (RAG, embeddings, vector DBs)
- Optimize AI models for performance, accuracy, and scalability
- Build intelligent agents and decision-making systems (Agentic AI)
- Integrate AI solutions with enterprise systems and observability platforms
- Handle model lifecycle management and advanced debugging

Governance & Security

- Ensure data privacy, security, and compliance in AI solutions
- Maintain proper access control for models and APIs
- Document AI workflows, models, and system architecture

Collaboration

Work with:

- IT Service Delivery teams
- Observability / Command Center
- Business stakeholders
- Identify opportunities to introduce AI-driven automation

Requirements

(a) Education: Bachelors degree in Computer Science / IT / Engineering or equivalent

Master’s degree in AI/ML/Data Science is preferred

(b) Experience: L2: 2–4 years in AI/ML / automation / chatbot development

L3: 4–8+ years in AI/ML, LLM, AIOps, or intelligent automation

(c) Certifications:





- Microsoft Azure AI / AI Engineer Certification – Preferred
- Google Professional ML Engineer – Preferred
- Certifications in Data Science / NLP / AI – Added advantage
- Python / ML certifications (Coursera, AWS, etc.) – Good to have

(d) Knowledge: Strong understanding of:

- AI/ML concepts (supervised/unsupervised learning)
- Natural Language Processing (NLP)
- LLM frameworks (GPT, OpenAI, Gemini, etc.)
- Knowledge of:
- RAG (Retrieval-Augmented Generation)
- Embeddings and Vector Databases (FAISS, Pinecone, Chroma)
- AIOps concepts:
- Anomaly detection
- Alert correlation
- Predictive monitoring
- Familiarity with:
- Observability tools (Prometheus, Grafana, ELK)
- APIs and microservices

(e) Skills:

- Robust programming skills in:
- Python (mandatory)
- REST API integration

Core Technical Skills

Experience with:

- LLM frameworks (LangChain, LlamaIndex)
- Chatbot development (Dialogflow, Rasa, Bot Framework)
- Open-source AI tools and libraries

Ability to build:

- Chatbots
- AI agents
- Knowledge assistants
- Automation pipelines using AI

Advanced Skills (L3 Preferred)

- Designing RAG-based architectures

Working with:

- Vector databases
- Prompt engineering
- Fine-tuning models

Experience with:

- Docker / containerization
- Cloud platforms (Azure, GCP)
- Integration of AI with ITSM and monitoring systems

Soft Skills

- Strong analytical and problem-solving skills
- Ability to translate business problems into AI solutions
- Excellent communication and stakeholder interaction
- Ability to work in high-pressure command center environments

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