Ampera Technologies

Role Overview

We are seeking a hands-on technology POD Lead who blends engineering excellence with statistical rigor and business acumen to drive end-to-end product delivery in an agile, data-driven environment. The ideal candidate will lead a multidisciplinary team of BI developers, data engineers, ML practitioners, and product analysts to accelerate business growth through scalable, AI-enabled products, econometric models and intelligent insights.

This role sits at the intersection of engineering, analytics, econometrics, MLOps and growth strategy, requiring a balance of technical depth, stakeholder engagement, and agile execution.

Key Responsibilities

1. Leadership & Delivery

• Lead a cross-functional pod of data engineers, BI developers, statisticians and machine learning engineers to deliver AI-powered products and analytics solutions.
• Translate strategic goals into data science roadmaps executed in agile sprints, ensuring measurable business outcomes for every release.
• Foster a culture of experimentation, accountability, and rapid iteration across data, AI, and product workstreams.

2. Product & Business Integration

• Partner with business stakeholders across Sales, Marketing, Finance, and Operations to identify high-impact use cases such as churn prediction, growth forecasting, pricing optimization, causal impact analysis or next-best-action recommendations.
• Drive the roadmap for analytical and econometric product capabilities (e.g., predictive dashboards, personalization engines, time-series forecasting, and more).
• Ensure all solutions are aligned with enterprise data strategy, governance, MLOps lifecycle and security standards.

3. Technical Execution

• Collaborate with ML engineers to productionalize models using Databricks, MLflow, Azure ML, or equivalent CI/CD MLOps frameworks.
• Guide teams on feature engineering, model selection, hyperparameter tuning, and validation for statistical and machine learning models.
• Encourage adoption of reusable data assets, API-based integrations, and modular code frameworks.
• Oversee econometric modeling, causal inference studies, and time-series forecasting for business-critical decision-making.
• Champion model lifecycle management, including version control, retraining pipelines, and performance drift monitoring.

4. Business Intelligence & Data Storytelling

• Supervise the creation of advanced BI dashboards and insight layers powered by predictive and generative AI.
• Translate complex statistical outputs into actionable business narratives for executive decision-making.
• Champion KPI alignment and measurement frameworks, ensuring analytics deliver quantifiable value to revenue, growth, retention, and operational efficiency metrics.

5. Agile Program Management

• Manage sprint planning, backlog prioritization, and resource allocation across concurrent projects.
• Track velocity, quality metrics, and ROI impact for each product stream.
• Coach teams on agile best practices and outcome-oriented delivery.

Qualifications

Required

• 10+ years of total experience with at least 3 years in a tech lead capacity.
• Proven expertise in Python, SQL, statistical modeling (e.g., regression, time-series, causal inference) and one or more of Power BI, Tableau, MicroStrategy.
• Strong foundation in data engineering, cloud architecture (Azure/AWS/GCP), and ML model deployment.
• Experience leading cross-functional agile teams with engineers, analysts, and data scientists.
• Excellent communication and stakeholder management skills — capable of simplifying complex data stories for business leaders.

Preferred

• Experience in forecasting models, econometrics, and experimental design (A/B testing, uplift modeling).
• Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Azure ML pipelines) and monitoring frameworks for models in production.
• Familiarity with agentic AI, LLM-based product development, or generative analytics use cases.
• Prior experience building analytics or AI solutions in B2B, SaaS, or digital transformation contexts.
• Certifications in Agile, Cloud (Azure ML, AWS Data Analytics), or Data Science specialization are a plus.

Key Traits

• Hands-on technologist who can code, review, and guide with empathy.
• Strategic thinker who connects product vision with execution.
• Comfortable operating in ambiguity and scaling solutions from POC to enterprise rollout.
• Passionate about mentoring teams and embedding a data-first, growth-oriented mindset.

About the Role

We are seeking a proactive and detail-oriented Site Reliability Engineer (SRE) with 3+ years of experience to ensure high availability, reliability, and performance of production systems.

This role focuses on automation, observability, incident management, and cross-team coordination to drive operational excellence.

Key Responsibilities

· Maintain reliable, scalable, and secure production environments.

· Implement and manage monitoring, alerting, and logging solutions.

· Contribute to defining and tracking SLIs/SLOs and support error budget practices.

· Automate operational tasks to improve efficiency and reduce manual effort.

· Perform troubleshooting and Root Cause Analysis (RCA) for production incidents.

· Optimize system performance, availability, and capacity.

· Maintain runbooks, SOPs, and incident documentation in Confluence.

· Adhere to change management, deployment governance, and disaster recovery standards.

· Support incident response for critical production services.

Collaboration & Tools

· Coordinate with external vendors and internal cross-functional teams.

· Work closely with Engineering, Product Owners, and Operations teams.

· Manage incidents and changes using ServiceNow & JIRA.

· Collaborate through Slack and structured communication channels.

Technical Skills

Systems & Cloud

· Strong knowledge of Windows and Linux/Unix systems.

· Solid understanding of networking fundamentals (DNS, TCP/IP, Load Balancing, Firewalls).

· Experience with at least one cloud platform (AWS, Azure, or GCP).

Automation & CI/CD

· Proficiency in one scripting/programming language (Python, Go, Bash, PowerShell, or Java).

· Understanding of CI/CD pipelines and automation practices.

Containers & Observability

· Hands-on experience with Docker and Kubernetes.

· Experience with monitoring tools such as Grafana or Power BI.

· Ability to analyze logs, metrics, and traces for troubleshooting.

ITSM & Documentation

· Experience with ServiceNow & JIRA (incident/change/problem workflows).

· Working knowledge of Confluence for technical documentation and knowledge management.

Additional Experience (Preferred)

· Background in DevOps, Cloud Engineering, or Platform Engineering.

· Understanding of security best practices and compliance standards.

· Familiarity with AI-assisted engineering tools (Claude Code, Jellyfish, GitHub Copilot).

· Exposure to large-scale or production-grade systems.

Soft Skills

· Strong analytical and troubleshooting mindset.

· Excellent written and verbal communication skills.

· Effective stakeholder and vendor coordination.

· Ownership-driven and composed during high-severity incidents.

Applying to jobs at Ampera is completely free. We never ask candidates for any payment.

About the Role

We are looking for a Customer Success Analyst who can work closely with Agile/Scrum teams and bring customer insights into product development. This role will act as a bridge between customers, product, and engineering teams by leveraging data from customer success platforms like Planhat.

Key Responsibilities

• Collaborate with Scrum teams (Product Owner, Developers, Scrum Master) during sprint planning and reviews
• Bring customer insights, feedback, and product usage data into backlog prioritization
• Monitor customer health scores, adoption metrics, and churn risks using tools like Planhat or Gainsight
• Translate customer challenges into actionable user stories and requirements
• Track feature adoption post-release and provide feedback to product teams
• Work closely with Customer Success, Sales, and Support teams to ensure alignment
• Maintain and analyze customer data in CRM tools like Salesforce or HubSpot
• Support renewal and retention strategies by identifying at-risk accounts

Key Requirements

• 5+ years of experience in Customer Success / Business Analysis / Product Support / Account Management
• Basic understanding of Agile / Scrum methodologies
• Experience with Customer Success platforms (Planhat, Gainsight, or similar)
• Strong analytical and problem-solving skills
• Ability to interpret customer data and convert it into actionable insights
• Good communication skills to work with cross-functional teams
• Experience working in SaaS or product-based environments preferred

Good to Have

• Exposure to Agile tools (Jira, Confluence)
• Experience with product analytics tools (Mixpanel, Amplitude)
• Understanding of customer lifecycle management and SaaS metrics (churn, retention, LTV)
• Exposure to accessibility and inclusive product design

Role Overview

We are seeking an experienced Power BI & Microsoft Fabric Consultant to partner with our BI team for a short-term engagement focused on capability building, governance setup, and platform enablement.

The primary objective of this role is to upskill the team and establish a robust, scalable foundation for Power BI and Microsoft Fabric—covering security, governance, administration, and best practices—so that the team can independently manage and scale the platform post-engagement.

This is a hands-on + coaching role, not just advisory.

Key Responsibilities

1. Power BI & Fabric Enablement

• Conduct structured training sessions and workshops on:
• Power BI (Pro + Fabric-integrated experiences)
• Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Pipelines, etc.)
• Build foundational understanding of:
• End-to-end analytics workflows in Fabric
• Integration between Power BI and Fabric workloads 
• Data Lake will be Snowflake

2. Governance & Security Framework

• Design and implement Power BI & Fabric governance model, including:
• Workspace strategy (Dev/Test/Prod separation)
• Naming conventions and standards
• Content lifecycle management
• Establish security architecture:
• Role-based access control (RBAC)
• Row-Level Security (RLS) / Object-Level Security (OLS)
• Data access patterns across Fabric and Power BI
• Define data sharing and access control processes

3. Administration & Platform Setup

• Configure and optimize:
• Power BI tenant settings
• Fabric capacity (capacity planning, workload management)
• Monitoring and usage metrics
• Set up:
• Deployment pipelines
• CI/CD best practices (where applicable)
• Audit logs and governance controls

4. Best Practices & Standards

• Define and document:
• Development standards (data modeling, DAX, report design)
• Performance optimization guidelines
• Dataset/reusable semantic model strategy
• Establish certification and promotion workflows for datasets and reports

5. Hands-On Implementation

• Work alongside the team to:
• Build or refactor key dashboards/reports using best practices
• Set up Fabric artifacts (Lakehouse/Warehouse/Pipelines)
• Ensure real use cases are implemented, not just theoretical training

6. Knowledge Transfer & Self-Sufficiency

• Provide:
• Playbooks, SOPs, and governance documentation
• Recorded sessions and training materials
• Mentor team members through:
• Office hours / Q&A sessions
• Code and architecture reviews
• Ensure the team can independently:
• Manage Fabric capacity
• Govern Power BI environment
• Implement secure and scalable solutions

Expected Outcomes (End of Engagement)

• Fully defined and implemented Power BI & Fabric governance framework
• Configured and optimized Fabric capacity + Power BI tenant
• Established security and access control processes
• Documented standards, playbooks, and operating model
• BI team capable of independent development, administration, and governance

Required Skills & Experience

Must-Have

• 5+ years of experience in Power BI development and administration
• Hands-on experience with Microsoft Fabric (end-to-end)
• Strong expertise in:
• Power BI governance and tenant administration
• Fabric capacity management
• Security models (RLS, RBAC, data access controls)
• Experience setting up enterprise BI governance frameworks
• Proven track record of training and mentoring teams

Good-to-Have

• Experience with data platforms (Snowflake, Azure, etc.)
• Knowledge of CI/CD for Power BI (DevOps integration)
• Familiarity with data catalog and lineage tools (e.g., Atlan, Alation)
• Understanding of modern data architecture patterns

Job Description: Scrum Trainer / Agile Coach

Role Title:

Scrum Trainer / Agile Coach

Location: Chennai/Bangalore - Remote / On-site (as required)

Employment Type: Full-time / Contract

About the Role

We are looking for an experienced Scrum Trainer or Agile Coach who can enable teams to adopt Agile practices effectively. The role involves training, coaching, and mentoring teams to improve delivery, collaboration, and agility.

Key Responsibilities

• Conduct Scrum and Agile training sessions for teams and leadership
• Coach teams on Agile frameworks including Scrum, Kanban, and Lean
• Facilitate Agile ceremonies (Sprint Planning, Daily Stand-ups, Retrospectives, Reviews)
• Drive Agile transformation and mindset across teams
• Identify gaps and implement process improvements
• Mentor Scrum Masters and Product Owners
• Track Agile maturity and performance metrics

Required Skills & Experience

• 4+ years of experience in Agile / Scrum roles
• Strong understanding of Scrum framework and Agile principles
• Experience in conducting training and workshops
• Excellent facilitation, communication, and stakeholder management skills
• Hands-on experience with tools like Jira, Azure DevOps, etc.

Certifications (Preferred)

• Certified Scrum Master (CSM)
• Professional Scrum Master (PSM)
• SAFe Agilist or equivalent Agile certifications

Preferred Experience

• Experience working with cross-functional tech teams
• Exposure to Agile transformation in organizations
• Experience in coaching distributed teams

Storytelling Specialist (Freelancer – PowerPoint & Narrative Design)

Role Title:

Storytelling Specialist – Presentation Design (Freelance)

Location: Chennai/Bangalore / Project-based

Engagement Type: Freelancer / Contract

About the Role

We are looking for a creative storyteller who can transform ideas, data, and concepts into compelling PowerPoint presentations. The ideal candidate should be able to craft narratives that engage, simplify, and influence diverse audiences.

Key Responsibilities

• Convert raw content, data, and briefs into structured, impactful presentations
• Build strong storytelling flow (problem → insight → solution → impact)
• Design visually appealing and professional PowerPoint decks
• Collaborate with stakeholders to understand messaging and audience
• Simplify complex concepts into easy-to-understand visuals and narratives
• Ensure consistency in branding, tone, and communication

Required Skills & Experience

• Proven experience in presentation design and storytelling
• Strong expertise in Microsoft PowerPoint / Google Slides
• Ability to create narratives for business, social impact, or consulting contexts
• Good understanding of visual design principles (layout, typography, color)
• Strong communication and interpretation skills

Preferred Experience

• Experience working in consulting, startups, or social impact sectors
• Exposure to DEI, accessibility, or inclusion-related storytelling
• Basic knowledge of data visualization

Deliverables

• High-quality PowerPoint decks
• Storyboarding documents (if required)
• Visual templates and reusable formats

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

Job Description:

1.     Machine Learning Development & Deployment

·      Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.

·      Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

·      Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2.     Cross-Functional ML Use Cases

·      Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

·      Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.

3.      Model Governance and MLOps

·      Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

·      Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

·      Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

·      Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration

·      Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

·      Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications:

Required

• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)

• 4+ years of experience in machine learning, data science.

• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.

• Experience deploying models into production using ML pipelines and orchestration frameworks.

• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.

• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).

• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

• Background in statistics, forecasting, optimization, or recommendation systems.

Hi ,

We are looking for Oracle Incentive Compensation & Order Management Techno-Functional Consultant

PFB the Job Description:

Job Title: Oracle Incentive Compensation & Order Management Techno-Functional Consultant

Experience: 5+ Years

Location: Remote

Key Responsibilities

Oracle EBS & Incentive Compensation

• Design, configure, and implement Oracle Incentive Compensation (OIC) solutions within Oracle EBS R12.
• Analyze and integrate sales compensation plans, quota management, and commission calculations.
• Configure Plan Elements, Rate Tables, Compensation Plans, Pay Groups, and Sales Rep structures.
• Develop and enhance commission and incentive reporting frameworks.

Technical Development

• Develop PL/SQL packages, procedures, and functions for enterprise applications.
• Build RICEW components (Reports, Interfaces, Conversions, Extensions, Workflows).
• Create XML Publisher (BI Publisher) reports using RTF and XSLT templates.
• Develop and enhance custom workflows and concurrent programs.

Integration & Cloud Technologies

• Design and implement integration solutions using Oracle Integration Cloud (OIC).
• Develop REST web services using Oracle EBS Integrated SOA Gateway (ISG).
• Build integrations between Oracle EBS and external applications (Salesforce, ERP Cloud, third-party systems).
• Implement inbound and outbound interfaces for enterprise data exchange.

Implementation & Support

• Participate in full lifecycle implementations, upgrades, and system stabilization projects.
• Conduct requirements gathering, PRD preparation, and functional/technical design documentation.
• Perform unit testing, integration testing, and UAT support.
• Provide production support and issue resolution for business-critical applications.

Data Migration & Bulk Data Handling

• Use SQL*Loader, Export/Import utilities, and data loaders (FBDI, HCM DL) for large-scale data migration.
• Manage data conversion from legacy systems to Oracle EBS/Fusion applications.

Technical Skills

ERP & Cloud Platforms

• Oracle E-Business Suite R12
• Oracle Incentive Compensation (OIC / ICM)
• Oracle Fusion ERP Cloud (Finance & SCM)
• Oracle Integration Cloud (OIC)

Development Technologies

• PL/SQL
• SQL
• XML / XSLT
• REST Web Services

Tools & Utilities

• TOAD
• PL/SQL Developer
• SQL*Loader
• Oracle Forms 6i
• Oracle Reports 6i
• BI/XML Publisher

Databases

• Oracle 9i / 10g

Functional Knowledge

• CRM Foundation
• Core HR
• P2P, O2C cycles
• Order Management, Inventory, Purchasing

Job Description:

We are seeking a Cloud & AI Platform Engineer to design and operate AI-native infrastructure that supports large-scale machine learning, generative AI, and agentic AI systems. 

This role will focus on building secure, scalable, and automated multi-cloud platforms across AWS, Azure, GCP, and hybrid on-prem environments, enabling teams to deploy LLMs, AI agents, and data-driven applications reliably in production. 

You will work at the intersection of cloud engineering, MLOps, LLMOps, DevOps, and data infrastructure, helping build platforms that support RAG pipelines, vector search, AI model lifecycle management, and AI observability. 

Key Responsibilities

AI & Agentic Infrastructure 

• Design infrastructure to support agentic AI systems, autonomous agents, and multi-agent workflows. 
• Build scalable runtime environments for LLM orchestration frameworks. 
• Enable deployment of AI copilots, assistants, and autonomous decision systems. 

Common frameworks may include: 

• LangChain 
• LlamaIndex 
• AutoGPT 

LLMOps & AI Model Lifecycle 

Design and manage LLMOps pipelines for the full lifecycle of large language models: 

• Model deployment 
• Prompt management 
• Versioning 
• Evaluation and testing 
• Model monitoring 

Integrate with AI platforms such as: 

• Azure Machine Learning 
• Amazon SageMaker 
• Vertex AI 

Retrieval-Augmented Generation (RAG) Infrastructure 

Design and optimize RAG pipelines that integrate enterprise knowledge with LLMs. 

Responsibilities include: 

• Document ingestion pipelines 
• Embedding generation workflows 
• Knowledge indexing 
• Query orchestration 
• Retrieval optimization 
• Support scalable semantic search architectures. 

Vector Database & Knowledge Infrastructure 

Deploy and manage vector databases used for AI applications and semantic retrieval. 

Common technologies include: 

• Pinecone 
• Weaviate 
• Milvus 
• FAISS 

Responsibilities include: 

• Index optimization 
• Query latency tuning 
• Scalable embedding storage 
• Hybrid search architecture 

Multi-Cloud AI Infrastructure 

Design and maintain AI-ready infrastructure across: 

• Amazon Web Services 
• Microsoft Azure 
• Google Cloud Platform 

Key responsibilities include: 

• GPU infrastructure management 
• Distributed training environments 
• Hybrid cloud integrations with on-prem data centers 
• Infrastructure scaling for AI workloads 

Data Platforms & Integration 

• Support deployment and optimization of data lakes, data warehouses, and streaming platforms. 
• Work with data engineering teams to ensure secure and scalable data infrastructure. 

Cloud Architecture & Infrastructure 

• Design and implement scalable multi-cloud infrastructure across Azure, AWS, and Google Cloud. 
• Build hybrid cloud architectures integrating on-premise environments with cloud platforms. 
• Implement high availability, disaster recovery, and auto-scaling architectures for AI workloads. 

DevOps, Platform Engineering & Automation 

Build automated cloud infrastructure using modern DevOps practices. 

Tools may include: 

• Terraform 
• Docker 
• Kubernetes 
• GitHub Actions 

Responsibilities include: 

• Infrastructure as Code (IaC) 
• Automated deployments 
• CI/CD pipelines for AI models and services 
• Platform reliability and scalability 

AI Observability & Monitoring 

Implement observability frameworks to monitor AI systems in production. 

This includes: 

• Model performance monitoring 
• Prompt evaluation 
• Hallucination detection 
• Latency and throughput analysis 
• Cost monitoring for LLM usage 

Tools may include: 

• Arize AI 
• WhyLabs 
• Weights & Biases 

Security, Governance & Responsible AI 

Ensure AI systems follow strong governance and security practices. 

Responsibilities include: 

• Data privacy and compliance 
• Model governance frameworks 
• Secure model deployment 
• Monitoring model bias and drift 
• AI risk management 

Support enterprise frameworks for Responsible AI and AI compliance. 

Data & Security 

• Experience with data lake architectures, distributed storage, and ETL pipelines 
• Knowledge of data security, encryption, IAM, and compliance frameworks 
• Familiarity with AI governance and responsible AI practices 

Required Skills 

Cloud & Infrastructure 

• Strong experience in Azure (must have), AWS or GCP 
• Hybrid and multi-cloud architecture 
• GPU infrastructure management 

DevOps & Automation 

• Kubernetes 
• Docker 
• Terraform 
• CI/CD pipelines 

AI / ML Platforms 

• MLOps pipelines 
• Model deployment 
• Model monitoring 

AI Application Infrastructure 

• Vector databases 
• RAG pipelines 
• LLM orchestration frameworks 

Programming 

Experience in one or more languages: 

• Python 
• Go 
• Java 
• TypeScript 

Preferred Qualifications 

• Experience building AI copilots or autonomous agents 
• Knowledge of distributed model training - Knowledge of GPU infrastructure and distributed training 
• Familiarity with AI evaluation frameworks - Familiarity with model monitoring, drift detection, and AI observability 
• Experience building enterprise AI platforms 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 
• 4–8+ years experience in cloud infrastructure, DevOps, or platform engineering 
• Experience working in data-driven or AI-focused environments 

What Success Looks Like 

• Reliable ML model deployment pipelines - Reliable infrastructure for LLMs and AI agents, Scalable RAG knowledge platforms 
• Efficient multi-cloud infrastructure management - Fast deployment cycles for AI products 
• Secure and scalable AI-ready cloud platforms 
• Strong automation and governance across cloud and AI systems