Senior Full-Stack Machine Learning Engineer - GenAI / Agentic Systems
New
P
PoppuloSoftware
Remote - IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSArtificial IntelligenceGCPMachine LearningAzureRESTful APIsPrompt EngineeringMLOpsGenerative AIComputer VisionDistributed Systems
Requirements
- Strong AI/ML background: Expertise in designing, building and deploying real-world ML applications (Computer Vision, Classification, etc.)
- Strong foundation in GenAI: Deep understanding of generative models (LLMs, etc.), AI Agents, prompt engineering RAG, vector databases
- Technical Expertise: Proficiency in ML/GenAI frameworks, databases, shell scripts and programming languages, including frameworks and tools commonly used for building and orchestrating AI agents
- MLOps expertise: Hands-on experience in setting up MLOps on AWS, Azure and GCP
- Full-stack expertise: Proficiency in data pipelines, distributed systems, APIs, web front-end, mobile apps, automated testing and cloud platforms (AWS, GCP, etc.)
- Exceptional problem-solving skills: Ability to simplify and breakdown complex technical and business challenges to create innovative and practical solutions
- Team management: Experience in guiding teams to deliver high-impact solutions
- Continuous learning: Ability to learn quickly and apply new technologies to solve problems practically
- Master’s or PhD in AI/ML or a related field
- At least 6+ years of experience building software or AI systems
- 1–3 years working with LLMs, generative AI, or AI agent architectures
Responsibilities
- Own end-to-end delivery of AI features across model, backend, APIs, UI integration, deployment, monitoring, and iteration in production
- Solve complex challenges with AI/ML: Design, develop new AI-powered products that deliver the product roadmap, including agentic AI solutions that orchestrate LLMs, tools, and workflows to solve multi-step problems autonomously
- Implement ML lifecycle - from data engineering and model development to cloud-based deployment, integrations and operationalisation, including MLOps
- Productionise full-stack AI/ML solutions: Translate emerging techs like GenAI & agentic AI architectures into innovative, practical solutions that transform customer experiences
- Align with Product Strategy: Create proof of concepts at high cadence to demonstrate/validate potential solutions as per our product strategy
- Optimise Model and system performance: Fine-tune, optimise training and inference performances, including latency, cost, and reliability trade-offs in agent-based and LLM-driven systems
- Wider collaboration: Partner with cross-functional teams to demonstrate and validate the impact of ML innovations before introducing them into the product ecosystem
- Research Savvy: Staying up-to-date with SOTA and industry trends in AI/ML, with a strong awareness of advances in agentic systems, autonomous workflows, and multi-agent architectures
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