Job Title Data & AI/Cloud Specialist
Category IT
Location Hyderabad, Pune, Bengaluru
Company techinterviews.ai
Description A hands-on technical expert who designs, builds, and operationalizes advanced AI solutions across the enterprise. You will lead end-to-end projects that span data engineering, large language models, generative and agentic AI, conversational interfaces (voice and chat), cloud-native deployment, and production-grade ML/LLM operations. This role suits candidates with 3 to 15 years of experience; hiring level and scope will be matched to years and demonstrated impact.


What you will do


Lead design and delivery of AI/ML solutions from problem framing, data ingestion, model development, evaluation, to production deployment and monitoring.
Build and maintain conversational systems including chatbots, voice bots, and contact-center AI integrations (CCAI, Genesys Cloud).
Develop and integrate generative AI and agentic AI capabilities (LLMs, retrieval-augmented generation, multi-step agents) into products and workflows.
Own MLOps / LLMOps pipelines: model versioning, CI/CD for models, automated testing, drift detection, and secure model serving.
Collaborate with cloud and platform teams to design secure, scalable architectures on AWS, Azure, and GCP.
Implement data platforms: data engineering, warehousing, governance, and analytics to support ML lifecycle and reporting.
Drive DevOps and DevSecOps practices to ensure secure, compliant, and observable deployments.
Mentor engineers and data scientists, contribute to solution and enterprise architecture, and support pre-sales and technical proposals when needed.

Core responsibilities


Translate business problems into data and AI solutions and produce measurable outcomes.
Architect data pipelines, ETL/ELT flows, and data models for analytics and ML training (Databricks, Spark/PySpark, Airflow, DBT).
Design and implement LLM-based systems: prompt engineering, fine-tuning, embeddings, retrieval layers, and agent orchestration.
Build conversational experiences using NLP, intent/entity extraction, dialog management, and voice technologies.
Implement model training, evaluation, and deployment workflows using MLOps/LLMOps tools and frameworks.
Ensure data governance, master data management, and data quality across pipelines (MDM, Informatica, Data Governance frameworks).
Integrate with enterprise systems: SAP, Oracle, Salesforce, and BI tools for reporting and operationalization.
Establish observability, performance testing, and QA for models and services.
Contribute to solution architecture, technical proposals, and agile delivery practices.

Required skills and technologies


Machine Learning and AI

Data Science; Generative AI; Agentic AI; LLMOps; MLOps; NLP; Computer Vision
Practical experience with LLMs, embeddings, RAG, fine-tuning, and agent frameworks.
Conversational Interfaces

Chatbot; Voice bot; Conversational AI; CCAI; Genesys Cloud
Experience building multi-channel dialog systems and integrating with contact center platforms.
Cloud and Platform

AWS; Azure; GCP design and deploy cloud-native ML services and infra.
DevOps; DevSecOps — CI/CD, infrastructure as code, security scanning, secrets management.
Data Engineering and Storage

Data Engineering; Big Data; Spark/PySpark; Databricks; Airflow; DBT; Snowflake; Data Warehousing; Data Architecture; Data Management
Hands-on with ETL/ELT, streaming/batch pipelines, and data modeling.
Databases and Integration

SQL/NoSQL; Oracle; SAP; MDM; SnapLogic; Informatica; Salesforce
Experience integrating enterprise data sources and APIs.
Programming and Frameworks

Python; Java; C++; .Net Stack — strong coding skills for production systems.
Django; Flask; FastAPI; REST API — building microservices and model-serving endpoints.
Analytics and BI

Data Analytics; Tableau; Power BI; Looker; MicroStrategy — dashboards, KPI tracking, and stakeholder reporting.
Tools and Platforms

Databricks; Dataiku; Snowflake; Airflow; DBT; Spark; SnapLogic; Salesforce; Genesys Cloud
Familiarity with QA/Testing and Performance Testing for services and models.
Methodologies

Agile/Scrum; Solution Architecture; Technical Architecture; Enterprise Architecture; PreSales — cross-functional collaboration and client-facing skills.


Experience and seniority guide


3–6 years (Mid-Level): Own modules of end-to-end ML projects; implement models, data pipelines, and conversational flows under guidance; deploy to cloud with CI/CD.
7–10 years (Senior): Lead projects, design architectures, mentor teams, drive MLOps/LLMOps adoption, and interface with stakeholders and pre-sales.
11–15 years (Principal/Architect): Define enterprise AI strategy, lead cross-functional architecture, own governance and security posture, and represent the organization in client engagements.

Qualifications


Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field; PhD optional.
Proven track record shipping production ML/AI systems and conversational agents.
Strong software engineering fundamentals and experience with cloud-native deployments.
Demonstrable experience with at least two cloud providers and multiple data/ML platforms.
Excellent communication skills and ability to translate technical trade-offs to business stakeholders.

Nice to have


Certifications in AWS/Azure/GCP, Databricks, or relevant vendor platforms.
Experience with enterprise MDM, Informatica, or large-scale SAP/Oracle integrations.
Background in contact center AI deployments and Genesys Cloud.
Publications, open-source contributions, or patents in AI/ML.Role & responsibilities
Salary Not Disclosed
Last Date 2026-02-15 00:00:00
Apply Link Click Here

© EllipseJob is Proudly Owned by