Location: Hyderabad ,India
Notice Period: Only Immediate joiners OR not longer than 30 days’ Notice Period
Experience: 5-8 Years
Employment Type: Full-Time , Work from Office
Job description:
The Senior AI Engineer will work on the design, development, testing, deployment, and continuous improvement of AI solutions for real business use cases. This role will contribute to proof-of-concepts, pilots, and enterprise-grade implementations across areas such as knowledge search, document intelligence, predictive analytics, computer vision, workflow automation, AI agents, and data-driven decision support.The position requires a hands-on engineer who can convert business requirements into working AI applications, collaborate with architects and business teams, integrate models with enterprise systems, and support solutions from initial prototype through deployment and operational support. Scope This role is intended for a technically strong and delivery-focused Senior AI Engineer with experience across AI/ML development, GenAI application engineering, data pipelines, model deployment, API integration, and cloud based AI platforms.
The Senior AI Engineer will be responsible for building reliable AI components, reusable engineering assets, and production-ready services while ensuring quality, performance, security, scalability, and maintainability.
The role will work closely with AI architects, solution teams, data engineers, application developers, and business stakeholders to deliver enterprise AI use cases in a structured and value-driven manner.
Responsibilities Main tasks include specifically:
- AI/ML Development: Design, build, train, evaluate, and optimize machine learning and deep learning models for business and enterprise use cases.
- Generative AI Engineering: Develop GenAI applications using LLMs, prompt engineering, embeddings, RAG pipelines, vector databases, document processing, and conversational AI patterns.
- Agentic AI Solutions: Build and integrate AI agents, tool-using workflows, multi-step reasoning flows, automation pipelines, and human-in-the-loop processes where required.
- PoC and Pilot Delivery: Develop proof-of-concepts, prototypes, and pilot solutions that demonstrate business value and can be extended into production-ready implementations. Flint International Middle East · Confidential · AI Engineering & Delivery Practice
- Enterprise Use Case Implementation: Convert business requirements into technical designs and working solutions for areas such as document intelligence, knowledge assistants, predictive maintenance, forecasting, anomaly detection, computer vision, and intelligent automation.
- Model Deployment and Integration: Deploy AI models and AI services through APIs, containers, cloud services, orchestration platforms, and integration layers connected to enterprise applications and data sources.
- Data Engineering Support: Work with structured and unstructured data, data pipelines, preprocessing, feature engineering, data validation, and integration with databases, data lakes, and enterprise systems.
- Performance and Quality: Evaluate model accuracy, response quality, latency, cost, reliability, observability, safety, and scalability; improve solutions based on testing and feedback.
- MLOps / LLMOps Practices: Support versioning, model registry, experiment tracking, CI/CD, monitoring, logging, evaluation, and controlled deployment of AI solutions.
- Technical Documentation: Prepare technical design notes, architecture inputs, deployment guides, API documentation, model evaluation summaries, and handover documents.
- Collaboration: Work with architects, data engineers, application teams, QA, DevOps, and business stakeholders to ensure smooth delivery and production readiness.
- Research and Innovation: Stay updated on AI frameworks, GenAI platforms, open-source tools, cloud AI services, model evaluation methods, and enterprise AI trends.
Required skills:
- AI / ML / DL: Strong understanding of supervised and unsupervised learning, deep learning, NLP, computer vision, model evaluation, feature engineering, and model optimization.
- Generative AI and LLMs: Hands-on experience with LLMs, embeddings, RAG, prompt engineering, vector databases, function calling, tool usage, document intelligence, and conversational AI.
- Agentic AI: Understanding of agent workflows, orchestration frameworks, planning, tool integration, memory, guardrails, and human-in-the-loop review patterns.
- Programming: Strong hands-on experience in Python and common AI/ML libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain, Llama Index, or similar frameworks.
- Data and Integration: Experience with SQL/NoSQL databases, APIs, ETL/ELT concepts, data preprocessing, data quality checks, and integration with enterprise systems.
- Cloud and Deployment: Experience with Azure, AWS, or GCP AI services, containers, REST APIs, model serving, CI/CD, and cloud-native deployment practices.
- MLOps / LLMOps: Knowledge of experiment tracking, model/version management, monitoring, evaluation, logging, automated testing, and Flint International Middle East · Confidential · AI Engineering & Delivery Practice controlled release practices.
- Security and Governance: Awareness of data privacy, secure API integration, access control, AI governance, responsible AI, model risk, and compliance considerations.
- Communication: Ability to explain technical concepts clearly, document solutions, work with cross-functional teams, and support business-facing technical discussions.
Required experience:
- Total experience of 6 to 10+ years in software engineering, AI/ML engineering, data science, cloud engineering, or enterprise application development.
- Minimum 3 to 5+ years of hands-on experience in AI/ML, deep learning, Generative AI, analytics, automation, or data-driven application development.
- Experience developing and deploying AI solutions from prototype to production, including APIs, integration, testing, monitoring, and handover.
- Experience working on enterprise PoCs, pilots, or production use cases involving business stakeholders, data sources, and application teams.
- Experience with one or more enterprise domains such as telecom, banking, manufacturing, healthcare, retail, government, utilities, or shared services is preferred.
- Experience in a remote or hybrid delivery model with strong ownership, documentation, and communication discipline.
Required education:
- bachelor’s degree in computer science, Data Science, Engineering, Information Technology, Artificial Intelligence, or a related field.
- Master’s degree in AI, Data Science, Computer Science, or a related discipline is preferred.
- Certifications in AI/ML, cloud platforms, data engineering, Generative AI, MLOps, or project delivery are preferred.