AI/ML Architect

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

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  Slackbots and Virtual Assistants

  
  


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  Conversational AI

  
  


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  Anomaly Detection

  
  


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  Event Correlation

  
  


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  AIOps for Infrastructure

  
  


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  Smart Answers

  
  


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  NLP/NLU

  
  


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  LLMs and Generative AI

  
  


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  Machine Learning and Advanced Analytics

  
  

Key Responsibilities

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  Lead the end-to-end machine learning project lifecycle, from experimentation and design to deployment and production support.

  
  


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  Architect and develop virtual assistants using Conversational AI technologies.

  
  


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  Design and implement AI/ML solutions for enterprise use cases.

  
  


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  Work with business stakeholders to identify business challenges that can be addressed using AI/ML.

  
  


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  Lead the development of advanced analytics solutions across different functional areas.

  
  


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  Design and implement AIOps solutions including clustering, classification, anomaly detection, event correlation, and capacity prediction.

  
  


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  Leverage LLMs and Generative AI technologies for enterprise applications.

  
  


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  Provide technical guidance and best practices on LLM and GenAI adoption, particularly for NLP and conversational use cases.

  
  


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  Design conversational flows, intents, entities, and dialogue experiences.

  
  


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  Integrate conversational AI and bot systems with backend applications, databases, APIs, and web services.

  
  


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  Lead complex and technically challenging AI/ML projects from concept to completion.

  
  


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  Collaborate with DevOps/SRE teams to build reliable and scalable AI/ML solutions.

  
  


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  Troubleshoot complex issues in distributed enterprise environments.

  
  


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  Drive innovation and identify opportunities for high-impact AI/ML solutions.

  
  


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  Mentor and provide technical guidance to engineering and AI/ML teams.

  
  

Required Skills

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  10–15 years of experience in AI/ML implementation and enterprise technology environments.

  
  


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  Strong understanding of supervised and unsupervised learning, deep learning, anomaly detection, and time-series forecasting.

  
  


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  Strong knowledge of LLMs, Generative AI, NLP, NLU, and Conversational AI.

  
  


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  Strong programming experience in Python.

  
  


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  Hands-on experience with Python libraries/frameworks such as Pandas, NumPy, TensorFlow or equivalent.

  
  


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  Minimum 5 years of experience in designing, developing, and deploying conversational bots/virtual assistants.

  
  


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  Strong experience in AIOps use cases such as:

  
  
  
  
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    Anomaly Detection

    
    
  
  
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    Clustering

    
    
  
  
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    Classification

    
    
  
  
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    Event Correlation

    
    
  
  
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    Capacity Prediction

    
    
  
  
  
  


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  Experience with Observability/Monitoring tools such as Prometheus, Splunk, Datadog, and Grafana.

  
  


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  Strong DevOps/SRE experience, preferably 5–7 years.

  
  


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  Experience integrating AI/bot solutions with backend systems, databases, APIs, and web services.

  
  


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  Strong troubleshooting and analytical skills in complex distributed environments.

  
  


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  Excellent communication, presentation, stakeholder management, and collaboration skills.

  
  


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  Ability to work independently and take ownership of complex technical initiatives.

  
  

Generative AI / LLM Skills

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  LLM application development

  
  


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  RAG architecture and implementation

  
  


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  Prompt engineering

  
  


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  LLM evaluation

  
  


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  Hallucination reduction

  
  


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  AI safety and guardrails

  
  


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  LLM deployment and scaling

  
  


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  Knowledge of fine-tuning techniques such as LoRA/adapters is desirable

  
  


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  Experience with AI agents/Agentic AI is desirable

  
  


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  Understanding of vector databases and semantic/hybrid search is desirable

  
  

Preferred Qualifications

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  Experience building enterprise-grade Conversational AI platforms.

  
  


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  Experience with RAG-based applications and LLM-powered assistants.

  
  


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  Experience with AIOps/IT Operations analytics.

  
  


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  Experience with observability platforms and infrastructure monitoring.

  
  


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  Experience with cloud platforms and production AI/ML deployments.

  
  


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  Experience with MLOps and model monitoring.

  
  


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  Experience working in complex, distributed enterprise environments.

  
  

Soft Skills

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  Strong problem-solving and analytical abilities.

  
  


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  Excellent verbal and written communication.

  
  


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  Strong stakeholder management skills.

  
  


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  Ability to lead technical discussions and architecture decisions.

  
  


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  Self-motivated and capable of working independently.

  
  


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  Strong leadership and mentoring capabilities.