About the Role :

We are looking for a highly skilled Senior Artificial Intelligence Engineer to design, develop, and deploy advanced AI solutions for enterprise applications and business use cases. The ideal candidate should have strong hands-on experience in Artificial Intelligence, Machine Learning, Generative AI, Large Language Models, Python, deep learning, and production-grade AI systems.

The role involves working across the complete AI lifecycle, from problem definition and experimentation to model development, deployment, monitoring, and optimization. The candidate will collaborate with product managers, data scientists, software engineers, data engineers, and technology leaders to build scalable and reliable AI solutions.

Key Responsibilities :

- Design, develop, and deploy AI and machine learning solutions addressing complex business and technical problems.

- Translate business requirements into AI/ML problem statements, solution approaches, and technical designs.

- Develop machine learning models for classification, regression, forecasting, recommendation, NLP, anomaly detection, and other relevant use cases.

- Build and productionize Generative AI solutions using Large Language Models and foundation models.

- Design and implement RAG-based applications using document processing, embeddings, vector databases, semantic search, and LLMs.

- Develop AI-powered applications, intelligent assistants, knowledge systems, and automation solutions.

- Experiment with different AI/ML algorithms, architectures, and models to identify optimal solutions.

- Develop and optimize deep learning models using appropriate frameworks and architectures.

- Work with structured and unstructured data and develop effective data preparation and feature engineering pipelines.

- Develop Python-based AI services, APIs, and reusable components for integration with enterprise applications.

- Integrate AI models with existing applications, databases, APIs, and enterprise technology platforms.

- Evaluate model performance using appropriate metrics and implement techniques to improve accuracy, reliability, and generalization.

- Optimize AI models and applications for latency, scalability, resource utilization, and cost.

- Implement model evaluation, monitoring, versioning, and lifecycle-management practices.

- Deploy AI/ML solutions into production environments and provide ongoing support and optimization.

- Troubleshoot model, data, integration, and production issues and perform root-cause analysis.

- Collaborate with software engineering teams to integrate AI capabilities into scalable applications and platforms.

- Work with DevOps and MLOps teams to establish automated model deployment and CI/CD pipelines.

- Ensure AI solutions meet requirements related to security, privacy, governance, reliability, and responsible AI.

- Conduct technical research and evaluate emerging AI technologies, models, frameworks, and tools.

- Develop Proofs of Concept and technical prototypes to validate new AI use cases.

- Create technical documentation covering model architecture, implementation, APIs, deployment, evaluation, and operational processes.

- Provide technical guidance and mentorship to junior AI/ML engineers.

Required Technical Skills:

- Strong hands-on experience in Artificial Intelligence and Machine Learning.

- Strong programming experience in Python.

- Strong understanding of machine learning algorithms, statistical concepts, and model evaluation techniques.

- Experience with deep learning frameworks such as PyTorch or TensorFlow.

- Strong knowledge of NLP and modern transformer-based architectures.

- Hands-on experience with Generative AI and Large Language Models.

- Experience with prompt engineering, embeddings, vector databases, and RAG architectures.

- Experience working with LLM APIs and/or open-source foundation models.

- Good understanding of model fine-tuning and customization techniques.

- Experience developing REST APIs and integrating AI solutions with enterprise applications.

- Strong understanding of data structures, algorithms, and software engineering principles.

- Experience working with SQL and databases.

- Good understanding of cloud-based AI/ML environments.

- Experience with Git, CI/CD, Docker, and modern software development practices.

AI & Generative AI:

- Experience designing production-grade LLM applications.

- Strong understanding of RAG architecture, including document ingestion, chunking, embedding generation, retrieval, reranking, and response generation.

- Experience with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or equivalent technologies.

- Experience with LLM evaluation, guardrails, hallucination mitigation, and response-quality optimization.

- Exposure to AI agents, tool calling, agentic workflows, and multi-step AI systems.

- Knowledge of multimodal AI involving text, images, documents, or other data types is desirable.

- Experience with LLM observability and production monitoring is an advantage.

MLOps & Production Engineering:

- Experience deploying AI/ML models in production environments.

- Understanding of model lifecycle management and MLOps practices.

- Experience with Docker and containerized AI applications.

- Exposure to Kubernetes and cloud-native deployment environments.

- Experience with CI/CD pipelines and automated deployment processes.

- Knowledge of model monitoring, logging, observability, and performance optimization.

- Experience with cloud platforms such as AWS, Azure, or GCP is desirable.

Preferred Skills :

- Experience with LangChain, LlamaIndex, Semantic Kernel, or similar AI frameworks.

- Experience working with OpenAI, Azure OpenAI, Anthropic, Gemini, Llama, Mistral, or other foundation models.

- Knowledge of AI agent frameworks and orchestration technologies.

- Experience with feature stores, ML platforms, or enterprise AI platforms.

- Exposure to Terraform or infrastructure-as-code practices.

- Knowledge of AI security, responsible AI, model governance, and data privacy.

- Experience building scalable AI platforms and reusable AI services.

Leadership & Collaboration :

- Take ownership of complex AI initiatives from solution design through production deployment.

- Provide technical leadership on AI/ML projects and contribute to architecture decisions.

- Mentor junior and mid-level AI engineers and promote best practices.

- Collaborate with product, engineering, data, cloud, security, and business teams.

- Present technical findings, solution approaches, and AI capabilities to technical and non-technical stakeholders.

- Evaluate technical trade-offs involving model accuracy, performance, scalability, security, and cost.

Key Competencies :

- Strong analytical and problem-solving skills.

- Deep understanding of AI/ML concepts and practical implementation.

- Strong software engineering and system-design capabilities.

- Ability to convert complex business problems into scalable AI solutions.

- Strong understanding of production AI and ML lifecycle.

- Excellent communication and stakeholder-management skills.

- Ability to work independently and lead technical initiatives.

- Strong focus on quality, scalability, security, and measurable business impact.

Education:

- A Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical discipline is preferred.