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AI Engineer

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Genzeon

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Location:
India , Pune

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

We're seeking an exceptional AI/ML Engineer who breaks the traditional mold. This isn't a role for someone who only trains models or lives in Jupyter notebooks. We need an end-to end product engineer who happens to have deep AI/ML expertise—someone who can architect scalable systems, ship production code, own product outcomes, and drive technical decisions from conception to deployment. You'll be responsible for building and scaling AI-powered products that directly impact our users and business. This means taking models from research to production, designing robust APIs, optimizing infrastructure, collaborating with cross-functional teams, and owning the complete product lifecycle. If you're a builder who thrives on seeing your work in users' hands and measures success by product impact rather than model accuracy alone, this role is for you.

Job Responsibility:

  • Product Development & Delivery: own entire AI/ML products from ideation to production
  • End-to-End ML Systems: Design and implement complete ML pipelines including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
  • Production Engineering: Write clean, tested, production-grade code across the stack
  • Build RESTful APIs, implement efficient data processing pipelines, optimize model serving infrastructure
  • Technical Architecture: Make critical architectural decisions around model selection, infrastructure design, data flow, and system integration
  • Cross-Functional Leadership: Collaborate with engineering, product, design, and business teams to translate requirements into technical solutions
  • Performance & Optimization: Continuously improve system performance, model accuracy, latency, and resource utilization

Requirements:

  • 4-6 years of software engineering experience
  • At least 3 years building and deploying AI/ML systems in production environments
  • Strong fundamentals in machine learning with hands-on experience across multiple domains (NLP, computer vision, recommendation systems, or time-series forecasting)
  • Proficiency with PyTorch or TensorFlow, scikit-learn, and modern ML frameworks
  • Solid proficiency in Python with experience in at least one additional language (Go, Java, JavaScript, or C++)
  • Deep understanding of data structures, algorithms, design patterns, and software architecture principles
  • Proven track record building scalable ML infrastructure including model serving (TensorFlow Serving, TorchServe, ONNX), feature stores, experiment tracking (MLflow, Weights & Biases), and CI/CD for ML
  • Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure)
  • Experience with backend development (FastAPI, Flask), API design, databases (SQL and NoSQL), caching strategies
  • Strong SQL and data manipulation skills with experience building ETL/ELT pipelines
  • Proficiency with data processing frameworks (Spark, Dask, or similar)
  • Ability to design robust, scalable systems considering performance, reliability, security, and cost
  • Experience with distributed systems, microservices architecture, and handling high-traffic production environments

Nice to have:

  • Experience with modern LLM frameworks (LangChain, LlamaIndex, Haystack), vector databases (Pinecone, Weaviate, Qdrant), and RAG architectures
  • Familiarity with model optimization techniques (quantization, pruning, distillation) and serving optimizations
  • Understanding of MLOps best practices and tools for model monitoring, versioning, and governance
  • You've built AI features that thousands or millions of users interact with daily
  • You've mentored other engineers and elevated team standards
  • You're comfortable with ambiguity and can scope and execute projects with minimal guidance
  • You stay current with AI/ML advances but know when to use proven approaches versus cutting-edge research
  • You have experience with A/B testing and experimentation frameworks
  • You've dealt with model drift, data quality issues, and production incidents, emerging with better systems and processes

Additional Information:

Job Posted:
January 05, 2026

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