Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong employer brand, metro location, and broad GenAI/MLOps requirements increase candidate competition.
Specialized GenAI and Agentic AI skills are transferable across industries but require specific AI experience.
Explicit 8-12 years plus mandatory GenAI, MLOps, and Agentic AI skills make shortlisting highly selective.
Build and deploy end-to-end scalable ML, GenAI, and Agentic AI systems focused on automation, productionization, and measurable business impact.
Design and optimize advanced AI architectures including RAG systems, autonomous multi-agent workflows, time series forecasting models, and scalable LLM inference.
Implement MLOps pipelines for CI/CD, model monitoring, drift detection, and integrate AI solutions with enterprise APIs and customer applications, while mentoring team members and collaborating with cross-functional Agile teams.
8-12 years of relevant work experience in AI/ML engineering or related fields.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field with 60%+ marks.
Proven expertise in building and deploying ML/Deep Learning, NLP, GenAI models, RAG systems, Agentic AI solutions, and time series forecasting models in production.
Experience with MLOps practices, cloud environments (preferably GCP), CI/CD, model monitoring, and enterprise AI integration.
Experienced in end-to-end AI system design and deployment who can drive automation and optimize for performance, cost, and latency in real-world business environments.
Capable of collaborating within Agile, cross-disciplinary teams including data scientists, product managers, and engineers, and mentoring junior team members.
Technically proficient with a strong ability to evaluate and incorporate emerging AI/GenAI tools, frameworks, and trends to maintain cutting-edge solutions.