Lead - Robotics Engineer

At InferQ, we are building solutions for autonomous and intelligent aerial systems. We seek a Robotic Navigation Engineer with deep technical rigor and a passion for developing mission-critical navigation, estimation, and path planning systems for UAVs. The role demands hands-on engagement across the autonomy stack. You will work at the intersection of robotics, AI, and aerospace, translating research into reliable field-deployable systems.

Key Responsibilities

• Architect and implement algorithms for joint state estimation, integrating multi-sensor inputs.
• Develop dynamic and real-time path planning solutions for UAVs.
• Create and validate flight simulations using various COTS Simulators or custom HIL/SIL test environments.
• Design and integrate advanced navigation systems.
• Work with avionics, AI, and controls teams to integrate autonomy modules on flight computers and mission systems.
• Conduct flight trials, performance evaluation, and algorithm optimization under real mission conditions.
• Support development of mission-critical autonomy frameworks for UAV systems.

Skillset/ Experience Required

• Strong foundation in linear and nonlinear state estimation and multi-sensor fusion.
• Expertise in path planning and trajectory optimization for aerial platforms.
• Proficiency in C++ / Python and ROS / ROS2 for robotic system development.
• Experience with aerospace kinematics and dynamics modeling, flight control integration, and navigation system design.
• Hands-on experience with Gazebo / X-Plane / AirSim / Unreal Engine simulators.
• Practical understanding of vision-based SLAM, visual odometry, and obstacle avoidance.
• Strong analytical and debugging skills, with a systems-engineering mindset.
• Exposure to AI-based perception systems.
• Integration of algorithms with embedded flight computers.
• Experience with HIL/SIL test setups and flight telemetry systems.
• Familiarity with DO-178C, DO-254, or other lifecycle processes is a plus.

Educational Qualification

• B.E. / B.Tech / M.Tech / Ph.D. in Aerospace, Robotics, Computer Science, Electrical, or related disciplines.
• Academic or project focus in Autonomous Systems, Estimation, Navigation, or AI-based Robotics is preferred.

About the Role

We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.

You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.

Example Project

Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:

• Generating client proposals using historical SharePoint data and CRM insights
• Summarizing meeting transcripts
• Drafting follow-up communications
• Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

• Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
• Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
• Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
• Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
• Drive architecture decisions balancing scalability, performance, and security
• Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
• Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

• 5+ years in full-stack development (Python backend + React/JavaScript frontend)
• Strong experience in API and microservice integration
• 2+ years leading technical teams and coordinating distributed engineering efforts
• 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
• Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

• Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
• Ensuring backend and AI systems are scalable, reliable, observable, and secure
• Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
• Experience building production-grade AI systems within enterprise SaaS ecosystems

About us

MyRico builds personal AI agents for enterprise, the copilots and digital employees that make humans more productive. The MyRico agents sit at the intersection of enterprise memory, high-end security, and an ever-expanding set of capabilities. We're a small team shipping fast, and the product is live with real customers today.

The role

Full-time · Bangalore

You'll own the systems that make an autonomous agent trustworthy in production. This is not just prompt engineering, and it's not model training - it's that and all the engineering layers in between: model steering, memory architecture, orchestration design, latency optimization, deterministic vs non-deterministic systems tradeoff, deployment, and operator tooling.

Concretely, the kind of work you'd have done here last week would include enhancing agent memory systems, runtime and scheduling reliability and predictability, adding new capability and tools to deployed agents, operator tools, live system debugging and benchmarking various models for price, latency and quality. And that is just last week. We are a small nimble startup rapidly working to address customer needs in this growing space, so things change rapidly.

What we're looking for

• More than 7 years of software engineering, with real production ownership of distributed or stateful systems - you've been paged for something you built and made it not happen again.
• Strong understanding of LLM based native app building, combining classic and model driven applications to get the best of both. You've built on LLMs beyond demos: agent frameworks, tool use, context management, eval fixtures, and you know why "it worked in the transcript" isn't evidence.
• Python and shell in production settings; comfortable in TypeScript/Node. You write boring, testable code and prefer the standard library to a new dependency.
• Systems taste: append-only logs, idempotent reconciliation, fold-the-events state machines, and read-only debugging surfaces feel like home.
• Evidence discipline: tests before features, claims backed by quoted observations, decisions written down.

Nice to have

• Experience running the combination of multi-tenant and single-tenant / on-prem-style fleets with ability to handle per-customer isolation, upgrade paths, migration compatibility in both setups.
• Security instincts for products that touch highly sensitive data and systems, including things like executives' email, calendars, and messages: least privilege, loopback-only services, secrets that never hit a log.
• You already orchestrate AI coding agents in your own workflow and have opinions about where they break.

How we work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation, your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.

We are building an advanced, AI-driven multi-agent software system designed to revolutionize task automation and code generation. This is a futuristic AI platform capable of:

✅ Real-time self-coding based on tasks  

✅ Autonomous multi-agent collaboration  

✅ AI-powered decision-making  

✅ Cross-platform compatibility (Desktop, Web, Mobile)  

We are hiring a highly skilled **AI Engineer & Full-Stack Developer** based in India, with a strong background in AI/ML, multi-agent architecture, and scalable, production-grade software development.

### Responsibilities:

- Build and maintain a multi-agent AI system (AutoGPT, BabyAGI, MetaGPT concepts)  

- Integrate large language models (GPT-4o, Claude, open-source LLMs)  

- Develop full-stack components (Backend: Python, FastAPI/Flask, Frontend: React/Next.js)  

- Work on real-time task execution pipelines  

- Build cross-platform apps using Electron or Flutter  

- Implement Redis, Vector databases, scalable APIs  

- Guide the architecture of autonomous, self-coding AI systems  

### Must-Have Skills:

- Python (advanced, AI applications)  

- AI/ML experience, including multi-agent orchestration  

- LLM integration knowledge  

- Full-stack development: React or Next.js  

- Redis, Vector Databases (e.g., Pinecone, FAISS)  

- Real-time applications (websockets, event-driven)  

- Cloud deployment (AWS, GCP)  

### Good to Have:

- Experience with code-generation AI models (Codex, GPT-4o coding abilities)  

- Microservices and secure system design  

- Knowledge of AI for workflow automation and productivity tools  

Join us to work on cutting-edge AI technology that builds the future of autonomous software.

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

Position Title: Real-Time Computer Vision & Edge AI Engineer (Founding Engineering Team / Core LLD) 

Reporting Structure: High-Level AI Architect (Principal ML Scientist, Google) 

Domain: Sub-16ms Edge AI, 3D Pose & Shape Estimation (SMPL-X), TensorRT C++ Inference, Zero-Copy Systems 

Performance Benchmark: Hard locked 60 FPS (<16.6 ms total frame budget) on dedicated RTX hardware

1. Position Overview & Architecture 

We are building a proprietary, ultra-low-latency spatial computing platform centered on high-fidelity 100% 3D Digital Twin architecture and real-time human digitization. 

In this role, you will serve as the Low-Level Design (LLD) Core AI Engineer, working directly alongside a Lead AI Scientist from Google. Your primary mandate is to solve complex surface occlusion and volumetric estimation challenges by building an ultra-fast C++ inference pipeline. This system must accurately regress a subject's true underlying 3D body shape and skeletal pose directly from a live camera feed. You will deploy models that extract parametric data (SMPL-X shape/pose parameters) and bridge these joint rotations seamlessly into our Vulkan graphics engine via shared GPU memory.

System Architecture: 

● Hardware Camera Ingestion: (V4L2 / GStreamer / CUDA) 

↓ Raw RGB Frames (Zero CPU Copy) 

● Edge AI Inference: (TensorRT / ONNX C++ API for 3D Pose Tracking, Kinematic Anchoring, SMPL-X Shape) 

↓ 3D Skeletal Transforms & Shape Parameters 

● Zero-Copy Shared Memory: (CUDA-Vulkan Bridge feeding directly into OpenRigLogic / MetaHuman Engine) 

2. Key Responsibilities & Deliverables 

A. Real-Time 3D Pose & Shape Estimation 

● Deploy and optimize state-of-the-art 3D human body reconstruction models (e.g., Shapy, SMPLify-X, CLIFF) to accurately regress the user's underlying skeletal structure and body volume, effectively bypassing unpredictable surface topologies and complex environmental occlusions. 

● Extract mathematically stable shape parameters (β) and pose parameters (θ) to drive the skeletal hierarchy of a high-fidelity digital avatar. 

B. Edge Inference Pipeline (TensorRT) 

● Translate Python-based research models into production-grade C++ inference engines using NVIDIA TensorRT and ONNX Runtime. 

● Implement INT8/FP16 quantization, layer fusion, and custom CUDA plugins to ensure the entire AI inference pass executes within a strict <10 ms budget per frame. 

C. Temporal Smoothing & Anti-Jitter Kinematics

● Implement highly optimized temporal filters (Kalman filters, One-Euro filters, optical flow tracking) in native C++ to eliminate all high-frequency jitter from the output joint rotations before they reach the graphics engine. 

● Ensure kinematic constraints (e.g., fixed bone lengths) are strictly maintained to prevent the digital asset from stretching or warping dynamically. 

D. Zero-Copy Ingestion & Engine Synchronization 

● Build hardware-accelerated video capture pipelines using V4L2 or GStreamer to ingest raw camera frames directly into GPU memory. 

● Bridge the output coordinate data and transformation matrices to the graphics team using POSIX shared memory and CUDA-Vulkan interop (VK_KHR_external_memory_fd), eliminating CPU staging overhead. 

3. Technical Qualifications & Tech Stack 

● Core Programming: Production-level Modern C++ (C++17/20), Python (strictly for model training/validation), and CUDA C/C++. 

● AI & Acceleration Frameworks: NVIDIA TensorRT, ONNX Runtime (C++ API), PyTorch. 

● Computer Vision Libraries: OpenCV (CUDA backend), MediaPipe C++ bindings. 

● Mathematical Foundations: 3D Kinematics, Matrix Transformations, Quaternions/Euler angles, statistical body modeling (SMPL/SMPL-X architecture). 

● Systems Architecture: Low-latency memory management, multi-threading (std::jthread, lock-free queues), SIMD vectorization. 

4. Relevant Projects & Demonstrable Experience (Preferred) 

Candidates will be preferred if they present functional codebases, GitHub repositories, or thesis work covering:

● Real-Time Body Fitting / Pose Estimation: Practical experience deploying 3D human pose or shape reconstruction models on live video feeds. 

● TensorRT / C++ Deployment: Demonstrable experience stripping a PyTorch model out of Python and running it natively in C++ using TensorRT or ONNX, ideally with custom CUDA layers or INT8 calibration. 

● High-Throughput Vision Pipelines: Built a C++ video processing pipeline that aggressively minimizes latency and avoids memory garbage collection pauses. 

● Kinematics & Smoothing: Applied mathematical filters to raw sensor or AI data to produce smooth, mechanically accurate 3D rotations. 

5. Compensation & Engagement Structure 

● Compensation: ₹1,50,000 to ₹2,00,000/month 

● Mentorship: Direct architectural guidance, algorithm review, and technical leadership from a Principal ML Scientist at Google. 

● Hardware: Dedicated high-end workstation equipped with discrete NVIDIA RTX hardware.

Key Responsibilities:

·      Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

·      Applied Mathematics &amp; Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

·      Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

·      Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

·      Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

·      Strategy &amp; Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

·      Education: Master&#39;s or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

·      Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

·      AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

·      Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

·      Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

• Strategic mindset with deep operational awareness.
• Excellent communication and stakeholder management skills.
• Ability to simplify complex technical concepts for executive reporting.
• Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

Technical Lead – Embedded Software Application Projects

link: https://www.ziliconcloud.com/careers

ZILICONCLOUD PRIVATE LIMITED is looking for an experienced Technical Lead with 5–8 years of industry experience in Embedded Systems, IoT, AI, Cloud, and Software Engineering.

The candidate will lead Embedded, IoT, Edge AI, and Cloud projects, define system architecture, convert requirements into technical tasks, and ensure successful project execution and delivery.

Key Responsibilities

• Design modular system architectures for Embedded, IoT, Edge AI, Cloud, REST APIs, and dashboards.
• Convert requirements into clear tasks, milestones, and deliverables.
• Track project progress, manage technical risks, and ensure on-time delivery.
• Guide engineers on coding standards, Git, testing, debugging, and documentation.
• Troubleshoot issues across hardware, firmware, IoT, cloud, backend, and UI.
• Support system integration, testing, debugging, and performance improvement.
• Review technical designs, code, and test results.
• Lead prototype development, integration, technical reviews, and demonstrations.
• Mentor young engineers and explain complex technical concepts in a simple and practical way.
• 
  

Required Skills

C/C++, Python, JavaScript/TypeScript, Linux, FreeRTOS, Microcontrollers, REST APIs, MQTT, Git, Embedded Software, IoT, and system debugging.

Preferred: AWS IoT, Microsoft Azure, ThingsBoard, OpenCV, TensorFlow Lite (TFLite), React, Flutter, or FastAPI.

Education

B.E./B.Tech/M.E./M.Tech in ECE, CSE, Embedded Systems, Mechatronics, or a related field.

📍 Location: East Tambaram, Chennai, Tamil Nadu

💼 Experience: 5–8 Years

🕒 Employment Type: Full-Time

Position: Guidance & Navigation Engineer

Experience: 1–3 Years

Location: Bengaluru, Karnataka

Employment Type: Full-time

About the Role

We are seeking a highly motivated Guidance and Navigation Engineer to join our autonomy and avionics team. The role involves developing, implementing, and validating state estimation, navigation, and guidance algorithms for autonomous UAVs operating in both GNSS-available and GNSS-denied environments.

The ideal candidate should have a strong foundation in estimation theory, sensor fusion, and navigation algorithms, along with hands-on experience in implementing these algorithms on embedded or real-time systems.

Key Responsibilities

• Design, develop, and optimise navigation and guidance algorithms for UAVs.
• Develop state estimation algorithms using IMU, GNSS, magnetometer, barometer, cameras, LiDAR, radar, and other onboard sensors.
• Design and implement estimation filters such as: EKF, UKF, Point-mass Filter
• Develop sensor fusion algorithms for robust localisation under degraded or denied GNSS conditions.
• Work on inertial navigation, dead reckoning, visual-inertial odometry (VIO), visual odometry (VO), and multi-sensor navigation systems.
• Develop guidance algorithms for waypoint navigation, path following, trajectory generation, and autonomous mission execution.
• Perform simulation, algorithm validation, and performance analysis using MATLAB/Simulink, Python, or C++.
• Analyse flight logs and sensor data to improve navigation accuracy and system robustness.
• Integrate navigation software with autopilots, embedded processors, and avionics systems.
• Support hardware-in-the-loop (HIL), software-in-the-loop (SIL), and field flight testing.
• Work closely with perception, controls, embedded software, and systems engineering teams.

Required Qualifications

• B.E./B.Tech/M.E./M.Tech in Aerospace Engineering, Robotics, Electronics, Electrical Engineering, Computer Science, Mechatronics, or a related discipline.
• 1–3 years of experience in navigation, estimation, robotics, autonomous systems, or UAV development.
• Strong understanding of: Linear Algebra, Probability and Statistics, Estimation Theory, Kinematics, Coordinate Systems, Control Systems,
• Experience developing estimation filters, especially EKF or UKF.
• Good understanding of IMU error modelling and inertial navigation.
• Experience with Camera, IMU Calibration and Synchronisation
• Experience with multi-sensor fusion.
• Strong programming skills in C++ and Python.
• Experience with MATLAB/Simulink for algorithm development and validation.
• Familiarity with Linux development environments.
• Experience with Git version control.

Preferred Skills

• Experience with Visual-Inertial Odometry (VIO), Visual Odometry (VO), or SLAM with loop closure techniques
• Knowledge of factor graph optimisation (GTSAM, Ceres Solver, g2o, etc.).
• Experience with ROS/ROS2.
• Familiarity with PX4 or ArduPilot.
• Experience working with embedded Linux or ARM-based processors.
• Understanding of UAV flight dynamics and autopilot architectures.
• Experience with flight data analysis and debugging.
• Exposure to airborne software development processes and coding standards (e.g., MISRA C++).

Personal Attributes

• Strong analytical and problem-solving skills.
• Ability to work independently on challenging technical problems.
• Good communication and documentation skills.
• Passion for robotics, autonomous systems, and aerospace technologies.
• Willingness to participate in field trials and flight testing.

This role offers the opportunity to work on cutting-edge UAV autonomy technologies from algorithm development through real-world flight validation.

Core Responsibilities

Operational Tools Architecture & Feature Planning 

• Own the feature roadmap for operational tools — Odoo, Redmine, GitHub, and adjacent systems — from business-process discovery through design, planning, and phased delivery 
• Serve as the subject-matter expert on how business processes (deal onboarding, delivery, support, finance, engineering operations) map onto tool capabilities, and where the gaps are 
• Design cross-tool workflows and integrations so that work items, code, and business records flow between systems as one coherent operational fabric 

Agentic Implementation & Modernization 

• Design agent-operable surfaces on each tool: typed, secure APIs, Model Context Protocol (MCP) servers, and webhook-driven automation that let AI agents read, create, and resolve operational work safely 
• Lead incremental modernization of legacy tool deployments — upgrading versions, retiring brittle customizations, and replacing manual processes with agentic workflows without disrupting live operations 
• Optimize agentic loops that operate on the tools for cost, speed, and integrity, with guardrails, audit trails, and human-in-the-loop checkpoints where they matter 

Plugin & Extension Engineering 

• Architect and build Redmine plugins in Ruby on Rails, Odoo modules, and GitHub Apps/Actions that extend the products cleanly rather than forking them 
• Engineer enhancements to be upgrade-safe across product versions — tracking upstream releases, isolating customizations behind stable interfaces, and planning migration paths 
• Prioritize contract-first API design between human intent, agent execution, and the underlying tools 

Governance & Quality Control 

• Act as the architectural reviewer for changes to Central tooling, including AI-generated pull requests and agent-authored configuration 
• Mandate testing and characterization coverage around customizations so upgrades and agentic changes land deterministically 
• Establish operational standards — access, data integrity, observability — for agents acting inside business-critical systems 

Required Qualifications 

• 10+ years in software engineering, business-systems architecture, or platform engineering, with deep SME knowledge of operational tools such as Odoo, Redmine, GitHub, or comparable ERP/ITSM/DevOps platforms 
• Expert-level proficiency in Ruby on Rails, including production experience building or maintaining Redmine plugins or comparable Rails-based extensions 
• Strong command of business processes across delivery, support, and operations — able to translate how a business runs into how a tool should behave 
• Practical agentic development experience: MCP, LLM tooling, AI-assisted automation, or agent-orchestrated workflows in production 
• Proven track record building plugins, modules, and extensions that survive product version upgrades, with disciplined API and integration design 
• Commitment to clean, tested, production-grade systems — including when the author of the code is an agent 

Preferred Qualifications 

• Python and Odoo module development experience; PostgreSQL fluency 
• Experience modernizing or migrating legacy tool deployments across major versions 
• Background in SaaS transformation, scale-up, or PE-backed environments