AI/ML Architect

Altraize; Hyderabad; On-site; INR 3,800,000 - 6,200,000; Full time

Job summary

Altraize is seeking an experienced AI/ML Architect to lead the design, development, and deployment of enterprise-grade AI/ML and Generative AI solutions. The role focuses on NLP/NLU, LLMs, RAG, and AIOps, with emphasis on integrating AI across enterprise systems.

The candidate will guide stakeholders, architect scalable AI solutions, and mentor teams through complex projects from concept to production.

Qualifications

• 10–15 years of experience in AI/ML implementation in enterprise environments.
• Strong background in ML, NLP/NLU, LLMs, and Generative AI.
• Proficiency in Python and ML libraries (Pandas, NumPy, TensorFlow).
• Minimum 5 years designing and deploying conversational bots/virtual assistants.
• Experience with AIOps concepts and observability tools.
• Experience integrating AI/bot solutions with backend systems.

Responsibilities

• Lead end-to-end ML project lifecycle from experimentation to deployment.
• Architect and develop virtual assistants using Conversational AI.
• Design and implement AI/ML solutions for enterprise use cases.
• Collaborate with business stakeholders to identify AI opportunities.
• Lead development of advanced analytics across functions.
• Design and implement AIOps including clustering, anomaly detection, event correlation.
• Leverage LLMs and Generative AI for enterprise apps.
• Provide guidance on LLM/GenAI adoption for NLP use cases.
• Design conversational flows, intents, entities, and dialogs.
• Integrate conversational AI with backend apps, databases, APIs.

Skills

AI/ML implementation

Python

Pandas

NumPy

TensorFlow

Conversational AI

NLP/NLU

LLMs/Generative AI

AIOps

DevOps/SRE

Time-series forecasting

Tools

Prometheus

Splunk

Datadog

Grafana

Job description

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.