AI Engineer

Title: AI/ML Test Engineer – GenAI

Location - Mumbai

Technical Skills

• Strong experience in Generative AI, LLMs, and Agentic AI systems

• Hands-on expertise with AI evaluation frameworks (RAGAS, DeepEval, TruLens, LangSmith, Promptfoo, etc.)

• Proficiency in Python and AI/ML development libraries

• Knowledge of Prompt Engineering, prompt testing, and optimization

Ability to define and track evaluation metrics such as accuracy, relevance, groundedness, hallucination rate, latency, and user satisfaction

• Experience in creating automated evaluation pipelines and benchmarking frameworks

• Strong understanding of AI safety, guardrails, bias testing, and responsible AI practices

• Familiarity with REST APIs, JSON, vector databases, and knowledge retrieval systems

• Experience in A/B testing, human-in-the-loop evaluation, and red teaming

• Strong experience in Manual Testing of AI/GenAI applications, including functional, exploratory, UAT, regression, and end-to-end testing

• Expertise in validating Agent Reasoning, Tool Calling, Workflow Execution, and Response Quality

• Hands-on experience in Automation Testing using Python frameworks

Key Responsibilities

• Design, execute, and automate evaluation strategies for Agentic AI applications.
• Develop evaluation datasets, test cases, and benchmark suites.
• Measure and improve agent performance, reasoning quality, tool usage, and workflow effectiveness.
• Analyze model outputs and identify hallucinations, biases, safety risks, and failure patterns.
• Collaborate with AI Engineers, Product Teams, and Domain Experts to improve agent quality and reliability.
• Generate evaluation reports, dashboards, and actionable recommendations.

About Us

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a QA Engineer who specializes in testing agentic AI platforms. You will design and automate quality processes for systems that involve LLMs, autonomous agents, tool use and orchestration across Lumen and Agent Studio — ensuring that AI-driven workflows behave reliably, safely and predictably in production, not just in a demo.

What You’ll Do

• Design and build automated test suites and evaluation frameworks for agentic AI workflows, including multi-step and tool-calling behaviors.
• Use AI/LLM-based QA tools and evaluation frameworks to test model outputs, agent decisions and end-to-end task completion at scale.
• Define quality metrics and benchmarks for agent reliability, correctness, latency and safety, and track them over releases.
• Identify edge cases, failure modes and regressions specific to non-deterministic AI systems, and build automated checks to catch them early.
• Integrate automated agent/LLM testing into CI/CD pipelines to support fast, reliable iteration.
• Partner closely with AI/ML and backend engineers to reproduce issues, root-cause failures and validate fixes.
• Work with customers and customer-facing teams to understand real-world usage patterns and translate them into test scenarios.

What We’re Looking For

• 2–4 years of QA/test automation experience, including hands-on work testing agentic AI or LLM-based platforms.
• Practical experience using AI-focused QA/evaluation tools to test agent behavior, prompts and model outputs.
• Strong scripting/automation skills (Python preferred) to build and maintain test frameworks.
• Understanding of how LLM-based agents work — tool calling, orchestration, memory, reasoning chains — well enough to design meaningful test cases.
• Comfort working with non-deterministic systems and designing evaluation approaches beyond traditional pass/fail testing.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience testing voice AI or real-time conversational systems.
• Familiarity with CRM or enterprise SaaS platforms.
• Exposure to enterprise security or compliance testing for AI systems.

Job title: Chief Agentic Quality Architect

Type: Full-Time | Contract

Location: Remote

Role Overview

Equity Partners builds profitable growth by acquiring and operating enterprise software companies — refining a proprietary operating model across 40+ acquisitions and two decades of hands-on experience, now supercharged by our patented agentic AI platform. We're hiring a Chief Agentic Quality Architect to lead the transition from traditional scripted testing to an AI-augmented quality ecosystem. In this role, you'll audit and rebuild our quality engineering foundations, deploy agentic automation across critical business flows, and build the guardrails needed to keep AI-generated code production-ready.

Key Skills

• 10+ years in QA automation engineering, SDET, or test architecture roles
• Expert-level proficiency in Playwright, Cypress, or Selenium
• Hands-on experience using LLMs (Claude, GPT-4, etc.) and agentic frameworks to generate code or automate workflows
• Deep understanding of integrating quality gates into AWS-based CI/CD pipelines or similar environments
• Architectural mindset, with the ability to design "Behavioral Snapshots" to safeguard critical business logic during rapid transformation

Responsibilities

• Conduct a comprehensive audit of the existing test estate across unit, integration, API, UI, sanity, and regression layers, producing a Current State & Gap Coverage Report
• Architect and own a phased quality engineering roadmap across two-week, one-month, and three-month delivery horizons
• Deploy agentic test generation — transforming business requirements into executable Playwright/Cypress scripts, generating synthetic test data, and implementing self-healing automation
• Design regression strategies and quality gates specifically tuned to catch hallucinations and logic errors in AI-generated code
• Build, mentor, and upskill a specialist QA team fluent in AI-assisted testing and agentic automation frameworks

If you're ready to rewrite the rules of quality for an AI-native world, we want you leading the charge.

EMBEDDED AI ENGINEERING POD

AI Implementation Engineer Role

Level: AI Implementation Engineer Senior / Advanced - 6+ years

Practice: Wissen GenAI

Locations: Mumbai / Bengaluru / New York - hybrid, embedded with delivery teams

Reports to: EMBEDDED AI PRACTICE Senior AI Engineering Specialist (Architect); Wissen GenAI Program Lead

Embedded inside enterprise delivery teams, you work closely with global, cross-regional teams to turn prioritized GenAI use cases into production software - building, integrating, and hardening Azure-based AI solutions and accelerating adoption within the teams you join.

You deliver production software and help the teams you join work faster.

As an embedded AI Implementation Engineer, you help convert prioritized use cases into shipped, governed, measurable software.

Key responsibilities

1. Build and ship.

Implement GenAI features end to end on Azure - RAG pipelines, agents, APIs, and UI integrations - against enterprise systems and data.

2. Embed and enable.

Work inside the delivery pods: pair with their engineers, remove blockers, and transfer GenAI skills so adoption sticks after you move on.

3. Productionize.

Add evaluation, observability, guardrails, caching, and CI/CD so prototypes become reliable, cost-efficient services.

4. Integrate securely.

Connect to enterprise data with correct access control, secrets management, and compliance with enterprise security standards and handling of sensitive data.

5. Iterate on quality.

Use evaluation results and user feedback to improve grounding, accuracy, latency, and cost.

6. Measure.

Track delivery and quality metrics that roll up to the program's targets.

Must-have qualifications

• 6+ years in software engineering, with 2+ years building GenAI/LLM applications in production.
• Strong Python (incl. async) and Java (the primary enterprise application stack; Spring a plus); solid API and systems design.
• Azure GenAI hands-on: Azure OpenAI, Azure AI Foundry, Azure AI Search for RAG, Azure AI Document Intelligence (IDP), and Prompt Flow.
• Agent frameworks: Microsoft Agent Framework / Semantic Kernel / AutoGen (or LangChain / LangGraph) and tool / function calling.

Preferred

• RAG fundamentals: embeddings, chunking, vector search, reranking, and grounding.
• Data platforms: Snowflake including Cortex AI (Cortex Search, LLM functions) and SQL, for accessing and grounding on enterprise data.
• Prompt engineering as versioned code; building and running evaluations.
• DevOps: Azure DevOps / GitHub Actions, Docker, AKS / Azure Functions, and observability.
• Financial services or other regulated environments.
• Front-end (React) for AI-assisted UX; streaming and token level operations.
• Azure AI Content Safety and responsible-AI practices.
• Certification: Azure AI Engineer Associate.

What success looks like - first 6 to 12 months

• Multiple GenAI features shipped to production within the embedded delivery pods.
• Measurable adoption and productivity uplift in the teams you support.
• Reusable components adopted from the architects' reference framework.
• Clear contribution to faster time-to-market and lower defect rates.

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

Required Skills:-

• 4+ years of experience in Software Testing (Manual & Automation).
• Strong experience with Selenium or Playwright.
• Hands-on experience in API Testing (Postman, REST Assured, Swagger, etc.).
• Experience with CI/CD pipelines (Jenkins, GitLab CI, Azure DevOps, GitHub Actions).
• Strong understanding of Functional, Regression, and Non-functional Testing.
• Excellent analytical and debugging skills.

AI-Specific Skills

• Hands-on experience in GenAI / AI Testing.
• Experience testing LLM-based applications.
• Strong understanding of prompt engineering and prompt validation.
• Knowledge of LLM behavior, model variability, and non-deterministic outputs.
• Experience validating AI outputs for accuracy, hallucinations, bias, and safety.
• Test data management for AI applications.
• Understanding of Responsible AI testing concepts.

About the team

SecurITe’s mission is to build an Agentic‑AI driven security platform that protects critical infrastructure from modern cyber threats. Our focus is on delivering highly performant, resilient, and intelligent network security systems that help defenders stay ahead of adversaries.

About the Role

We’re looking for a seasoned Senior Quality Engineer to provide technical leadership and architectural oversight for our next‑generation cybersecurity AI platform. In this high-impact role, you will define the technical strategy for quality assurance, ensuring our agentic AI transforms cyber defense with unparalleled reliability.

You will be responsible for the end-to-end quality lifecycle, from architectural reviews to the deployment of scalable automation frameworks. Beyond technical execution, you will serve as a mentor to junior team members, fostering a culture of technical excellence and driving the strategy that ensures our solutions meet the rigorous demands of critical infrastructure protection.

What You’ll Do

●    Defining and driving comprehensive QA strategies and roadmaps for the cybersecurity platform.

●    Designing, developing, and executing test plans, test cases, and automated scripts to ensure software quality.

●    Performing functional, regression, performance, scalability and security testing to identify bugs or defects.

●    Collaborating with developers, product managers, and other stakeholders to understand product requirements and testing needs.

●    Identifying, documenting, and tracking software defects, ensuring clear communication of issues and their resolutions.

●    Leading deep-dive root-cause analysis for critical system defects and security vulnerabilities.

●    Conducting thorough reviews of product specifications and software design to identify potential areas of concern before testing.

●    Architecting and designing complex, scalable test automation frameworks to optimize CI/CD velocity.

●    Ensuring the software meets customer and business requirements by validating the functionality and performance.

●    Assisting in continuously improving QA processes, tools, and best practices to enhance software testing efficiency and effectiveness.

●    Supporting user acceptance testing (UAT) and assisting clients with product validation.

●    Mentoring junior and mid-level engineers, providing technical guidance and conducting architectural reviews.

Required Experience

●    A Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, or a related field.

●    8-10 years of proven experience in quality engineering, specifically within network cybersecurity, Identity Providers, or AI-integrated platforms.

●    Expertise in manual and automated testing.

●    Deep domain expertise in complex system validation and advanced automation practices at scale.

●    Proficiency in programming languages like Python to build and run automated test scripts.

●    "Strong knowledge of software testing methodologies, performance testing tools (e.g., JMeter, k6), and security traffic generation/simulation tools (e.g., Ixia BreakingPoint, Scapy, or Snort/Suricata traffic generators)."

●    Understanding of continuous integration/continuous deployment (CI/CD) pipelines and version control systems like Git.

●    Strong communication skills for documenting test results and interacting with cross-functional teams.

●    Excellent analytical skills, attention to detail, and problem-solving ability.

●    Ability to work independently as well as collaboratively in a team environment.

●    A curious mindset with a willingness to quickly learn new technologies and testing tools.

Required Skills & Qualifications

●    Familiarity with cloud-based testing environments (GCP, AWS, Azure).

●    Experience with cybersecurity products or cloud services or IDP or Web UI

The Mindset

●    Problem Solver: You thrive on complex, ambiguous challenges and engineer elegant solutions.

●    Ownership‑Driven: You take initiative, move fast, and deliver outcomes without hand‑holding.

●    Continuous Learner: You stay ahead of the curve in AI, ML, and emerging technologies.

●    Startup DNA: You excel in fast‑moving environments where priorities evolve and impact is immediate.

Job Summary/ Job Opportunity:

This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde

Key Objectives and Major Responsibilities:

• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team

Key Capabilities and Competencies:

Knowledge, Skills, Qualification and Experience

• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies. 

Position Overview 

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems. 

Responsibilities 

• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms 
• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning 
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection 
• Deploy, maintain and monitor containerized ML workloads 
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows 
• Implement AI-driven data validation, schema and concept drift detection and metadata management. 
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability 
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention 
• Develop automated test suites for model performance, regression, edge cases and bias validation 
• Monitor model KPIs (accuracy, precision, recall, latency, calibration) 
• Ensure reproducability of experiments and production models 
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption 

Qualifications 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field 
• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering 
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure 
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn