AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : AI & Data Solution Architecture
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary
Job Title: FullStack AI/ML Engineer - Cognitive Platform
We're looking for a visionary Lead Full Stack Engineer with at least 4-5 years of hands-on experience in building cognitive, AI/ML-driven platforms across on-prem HCIs, private cloud, and public cloud stacks, to work on business-critical client environments. This lead-level role requires strong architectural solutioning ownership, statistical and analytical depth, and proven experience delivering end-to-end production AI systems, including time-series forecasting, recommendation engines, clustering, NLP, and deep learning solutions. The role involves technical leadership of engineering teams, direct interaction with enterprise clients, and ownership of solution design, delivery, and execution. The focus is to manage and evolve client technology landscapes, lead cross-technology migrations and modernization initiatives, handle technology crisis management, and deliver measurable business value through innovation while addressing complex technical and organizational challenges.
Roles and Responsibilities:
Act as a deep technical specialist for AI/ML-driven cognitive platforms, owning solution design, advanced problem solving, and architectural decision support across engagements.
- Architect and deliver end-to-end AI/ML solutions, covering problem formulation, statistical modelling, feature engineering, model development, evaluation, production deployment, and post-deployment optimization.
- Apply strong statistical foundations (probability, hypothesis testing, regression, time-series analysis) to design robust, explainable, and reliable ML systems for enterprise use cases.
- Design and implement knowledge-driven architectures, including knowledge graphs, entity relationship modelling, semantic layers, and decision intelligence frameworks.
- Build and productionize time-series forecasting systems, recommendation engines, clustering and segmentation models, anomaly detection pipelines, NLP, and deep learning solutions.
- Define and implement advanced MLOps practices, including CI/CD for ML, model versioning, monitoring, drift detection, retraining strategies, scalable inference, governance, and Responsible AI guardrails.
- Provide architectural solutioning expertise, translating complex business and data problems into scalable, resilient, cloud-native system designs.
- Design, develop, and optimize end-to-end cognitive platforms on HCIs and private cloud, integrating AI intelligence into traditional web and enterprise stacks.
- Architect and build full-stack infrastructure platforms, including backend services (APIs, microservices) and API gateways.
- Provide specialist guidance on GPU-based computing and High-Performance Computing (HPC) environments supporting AI/ML workloads.
- Contribute expertise to AWS Outposts, Azure Stack, and Google Cloud VPC-based hybrid architectures.
- Design and support Tanzu and Red Hat OpenShift cluster deployments on private cloud environments.
- Develop cloud-native backend services using Python (FastAPI/Flask), Node.js, or Java to integrate AI models with application logic.
- Integrate AI/ML models (TensorFlow, PyTorch, scikit-learn) into secure, scalable, production-grade APIs and microservices.
- Ensure high code quality, performance, and maintainability, and seamless integration between front-end interfaces and backend services.
- Implement and refine Infrastructure as Code (IaC) using Terraform, CloudFormation, Azure DevOps, Pulumi, or GCP-native tools.
- Design and support Platform-as-a-Service (PaaS) offerings.
- Architect and optimize database solutions, including relational and NoSQL systems.
- Ensure platform observability and reliability using monitoring, logging, and metrics frameworks (Prometheus, ELK, CloudWatch).
- Support containerization and orchestration strategies using Docker and Kubernetes.
- Work directly with clients and senior stakeholders as a trusted technical specialist, contributing to design reviews, architecture discussions, and critical issue resolution.
- Integrate and support AI-driven tools and frameworks, including Generative AI and Agentic AI technologies, within enterprise cloud platforms.
Telecom domain expertise:
- Strong understanding of mobile network technologies, including 2G, 3G, 4G, and 5G architectures, RAN protocols, and radio propagation principles.
- Mandatory expertise in radio network planning and design concepts and methodologies.
- Hands on experience with OSS platforms from major OEMs such as Ericsson, Nokia, and Huawei, with expert level knowledge of RAN/Core OSS KPIs and practical exposure to KPI optimization.
- Experience in customer experience management within telecom networks, including understanding of multiple data sources such as crowd-sourced data, drive test data, probe data, network traces, and strong capability to correlate these datasets for insights.
- Knowledge of emerging telecom technologies, including Open RAN architecture, RIC (Near RT/Non RT), R Apps, xApps, and understanding of their integration and impact on legacy network environments.
- Strong understanding of OEM-specific radio network parameters, counters, and KPIs, along with knowledge of radio features and the latest 3GPP releases.
- Experience with SON (Self Organizing Networks) modules from any Tier 1 OEM, including configuration, operation, and optimization of SON functionalities.
Professional and Technical Skills:
Systems Operations and Engineering RHEL and SUSE and OLE and/or Wimdows/Hperv and/or AIX/HPUX/Soalris
Hyper Converged Infrastructure and Private Cloud Vmware/ Hyper V/ KVM//Pacemaker / AHV /OpenStack / Scale Computing / Nvidia Omniverse
Infrastructure as a Code & Scripting Terraform or Ansible - Shell Scripting and Python, Pwer Shell, Power CLi
Public Cloud IaaS and Associated PaaS AWS / Azure/ GCP - S3, Blobs, VPCs, vNet, LBs Cloud Watch, Stack Driver, Azure Mon
Cloud Native and Containers PODMAN, Docker Openshift and AKS and GKE and EKE
Database Systems (RDBMS, No SQl and Cloud DB) Orcle and MS SQL or MySQL and Mongo or Couch - Cloud DB (Redis or Aurora, Sql online
Midleware (WebServers, Message Qs, Managed Apps, MFT, Job Schedulers IIS, Apache, JBOSS, WebSphere/ Web Logic MFTs, MQ Series, Control M or Autosys or TWS and MTFs
Observability & Environment Health observability tools (Nagios /SolarWinds/Netcool, Prometheus, ELK) and Environment Health and Capacity Management and Tech Debt and FinOps
Enterprise AI Agentic AI Framework (CrewAI, LangGraph, AutoGen) and Responsible AI Concepts and AI Guardrails
- Additional Information:
- Systems Operatioms and Engineering Professional
- VMware Certified Cloud Expert -the VMware Certified Professional VMware Cloud (VCP VMC) 2022
- RHCE ( Red Hat Certified Engineer)
- Nvdia Certified Engineer / Nvidia Certified Associate
- Microsoft Certified: Azure Solutions Architect Expert
- Google Professional Cloud Architect /Machine Learning
- Certified Kubernetes Administrator (CKA)
- HashiCorp Certified: Terraform Associate
- Certified DevOps Engineer certifications (AWS, Azure, or Google)
- AI Powered Tech Talent
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