Join our Team

About this opportunity:

An AI/ML Solution Architect designs the end-to-end technical blueprint for artificial intelligence and machine learning systems, bridging the gap between business objectives and engineering execution. 

What you will do:

• Primary Mission: Decide how ML models, generative AI components, data pipelines, and cloud infrastructure work together securely and cost-effectively at scale.
• Distinction: Unlike data scientists (who build/evaluate models) or AI engineers (who write application code or APIs), architects design the entire ecosystem the models live in.
• Time Allocation: Heavily focused on system design, stakeholder alignment, risk management, and cross-functional leadership.
• Solution Design: Architect scalable, production-grade AI/ML systems including Retrieval-Augmented Generation (RAG), multi-agent orchestration, and real-time inference.
• Technology Selection: Choose appropriate cloud platforms (AWS, Azure, GCP), foundation models (1st-party, 3rd-party, open-source), and vector databases (Pinecone, Milvus, pgvector).
• MLOps & Governance: Establish CI/CD pipelines, model monitoring, drift detection, data lineage, and regulatory/ethical compliance.
• Stakeholder Management and Engagement: Translate vague business goals into technical requirements, lead RFP/RFI responses, and present architecture to executives. 

The skills you bring:

• Languages & Frameworks: Python, PyTorch, TensorFlow, LangChain, LlamaIndex Pexpect.
• MLOps & Data: MLflow, Kubeflow, Weights & Biases, Apache Spark, Airflow, feature stores.
• Cloud Services: AWS (SageMaker), Azure (Azure ML / AI Search), or Google Cloud (Vertex AI). Certifications (Preferred): AWS Certified Solutions Architect / Machine
• Learning Specialty, Microsoft Azure AI Engineer/Solutions Architect, or Google Cloud Professional.
• Soft Skills: Problem framing, cross-team orchestration, and executive communication. 
• Other Skills: Coordinate across domains to resolve multi-layer technical dependencies.
• Collaborate with architects, field teams, vendors, and customer stakeholders.
• Provide governance for upgrades, configurations, integrations, and change activities.
• Ensure compliance with SLAs and KPI performance.
• Drive automation to improve efficiency .
• Identify recurring issues and risks before impact.
• Initiate corrective actions and process enhancements.
• Stay updated with emerging technologies and industry practices.
• Communicate complex technical issues clearly to leadership and stakeholders.
• Prepare technical reports, RCA summaries, and performance insights.
• Mentor engineers and other teams.
• Telecom Domain knowledge is an added advantage
• Experience: Typically 8+ years in software engineering and 3+ years hands-on delivery of ML/AI projects into production.

“All academic credentials must be from recognized and accredited institutions and are further subject to verification.”