AI/ML Architect

Experience: 10–15 Years
Location: Hyderabad
Work Mode: On-site
Interview Mode: Face-to-Face (F2F)
Role Summary
We are looking for an experienced AI/ML Architect to lead the design, development, and deployment of enterprise-grade AI/ML and Generative AI solutions. The ideal candidate will have strong experience in Machine Learning, Conversational AI, NLP/NLU, LLMs, RAG, AIOps, DevOps/SRE, and enterprise system integration.
The candidate will work closely with business and technical stakeholders to identify AI/ML opportunities, architect scalable solutions, and lead technically complex projects from concept through implementation.
Key Areas of Focus
Slackbots and Virtual Assistants
Conversational AI
Anomaly Detection
Event Correlation
AIOps for Infrastructure
Smart Answers
NLP/NLU
LLMs and Generative AI
Machine Learning and Advanced Analytics
Key Responsibilities
Lead the end-to-end machine learning project lifecycle, from experimentation and design to deployment and production support.
Architect and develop virtual assistants using Conversational AI technologies.
Design and implement AI/ML solutions for enterprise use cases.
Work with business stakeholders to identify business challenges that can be addressed using AI/ML.
Lead the development of advanced analytics solutions across different functional areas.
Design and implement AIOps solutions including clustering, classification, anomaly detection, event correlation, and capacity prediction.
Leverage LLMs and Generative AI technologies for enterprise applications.
Provide technical guidance and best practices on LLM and GenAI adoption, particularly for NLP and conversational use cases.
Design conversational flows, intents, entities, and dialogue experiences.
Integrate conversational AI and bot systems with backend applications, databases, APIs, and web services.
Lead complex and technically challenging AI/ML projects from concept to completion.
Collaborate with DevOps/SRE teams to build reliable and scalable AI/ML solutions.
Troubleshoot complex issues in distributed enterprise environments.
Drive innovation and identify opportunities for high-impact AI/ML solutions.
Mentor and provide technical guidance to engineering and AI/ML teams.
Required Skills
10–15 years of experience in AI/ML implementation and enterprise technology environments.
Strong understanding of supervised and unsupervised learning, deep learning, anomaly detection, and time-series forecasting.
Strong knowledge of LLMs, Generative AI, NLP, NLU, and Conversational AI.
Strong programming experience in Python.
Hands-on experience with Python libraries/frameworks such as Pandas, NumPy, TensorFlow or equivalent.
Minimum 5 years of experience in designing, developing, and deploying conversational bots/virtual assistants.
Strong experience in AIOps use cases such as:
Anomaly Detection
Clustering
Classification
Event Correlation
Capacity Prediction
Experience with Observability/Monitoring tools such as Prometheus, Splunk, Datadog, and Grafana.
Strong DevOps/SRE experience, preferably 5–7 years.
Experience integrating AI/bot solutions with backend systems, databases, APIs, and web services.
Strong troubleshooting and analytical skills in complex distributed environments.
Excellent communication, presentation, stakeholder management, and collaboration skills.
Ability to work independently and take ownership of complex technical initiatives.
Generative AI / LLM Skills
LLM application development
RAG architecture and implementation
Prompt engineering
LLM evaluation
Hallucination reduction
AI safety and guardrails
LLM deployment and scaling
Knowledge of fine-tuning techniques such as LoRA/adapters is desirable
Experience with AI agents/Agentic AI is desirable
Understanding of vector databases and semantic/hybrid search is desirable
Preferred Qualifications
Experience building enterprise-grade Conversational AI platforms.
Experience with RAG-based applications and LLM-powered assistants.
Experience with AIOps/IT Operations analytics.
Experience with observability platforms and infrastructure monitoring.
Experience with cloud platforms and production AI/ML deployments.
Experience with MLOps and model monitoring.
Experience working in complex, distributed enterprise environments.
Soft Skills
Strong problem-solving and analytical abilities.
Excellent verbal and written communication.
Strong stakeholder management skills.
Ability to lead technical discussions and architecture decisions.
Self-motivated and capable of working independently.
Strong leadership and mentoring capabilities.
Skills: design,nlp,ai,anomaly detection,ml

Work arrangement
No