1 day ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Lead the architecture, development, deployment, monitoring, and optimization of production AI and ML products.
- Build NLP, LLM, prompt engineering, RAG, and fine-tuning solutions for code modernization, technical documentation, and intelligent automation.
- Architect resilient, serverless AI microservices and batch pipelines on Google Cloud Platform.
- Implement AI workload tagging, billing telemetry, cost attribution, token metering, model routing, and inference-cost optimization.
- Develop code parsing, AST analysis, source-to-source transformation, and technical artifact-generation capabilities.
- Establish CI/CD pipelines, automated testing frameworks, robust APIs, model tracking, and technical documentation.
- Collaborate with product managers, UX designers, software architects, and domain experts to integrate AI solutions into enterprise ecosystems.
- Mentor junior team members and promote production ML engineering best practices.
Requirements
- 4–7 years of hands-on experience building, deploying, and scaling end-to-end AI/ML products, generative AI applications, code transformation tools, and intelligent software automation systems.
- Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related quantitative field.
- At least one recognized cloud or AI certification, such as Google Cloud Professional Machine Learning Engineer or an equivalent advanced AI or cloud credential.
- Extensive expertise with Google Cloud Platform and services including Vertex AI, Cloud Run, BigQuery, Cloud Functions, and GKE.
- Strong proficiency with LLMs, RAG architectures, vector databases, and frameworks such as Pinecone, ChromaDB, Vertex Vector Search, LangChain, and LlamaIndex.
- Mastery of Python and solid experience with backend stacks, gRPC, microservice design, and modern frontend frameworks.
- Familiarity with abstract syntax trees, static code analysis, compiler concepts, and domain-specific language translation.
- Hands-on experience with MLOps, model tracking, automated testing, Docker, Kubernetes, and CI/CD pipelines.
- Experience with token metering, cost attribution, model routing, GPU or TPU utilization, OpenTelemetry, and Cloud Monitoring.
- Preferred experience with source-to-source compilers, automated code conversion, design-document generation, legacy enterprise frameworks, quantitative product analytics, or an AI-focused GitHub portfolio.
- Strong communication, product thinking, problem-solving, user advocacy, and collaboration skills.
Benefits
- Permanent employment with Airbus India Private Limited.
- Flexible working arrangements are supported where possible to make work, connection, and collaboration easier.