Description

Key Responsibilities

Design, develop, and deploy scalable AI systems and intelligent applications for enterprise use cases.

Architect and implement AI/ML solutions, including LLM-powered applications, generative AI workflows, and AI-driven automation.

Develop and maintain backend services, APIs, and system integrations that support AI capabilities.

Evaluate, integrate, and optimize machine learning models and foundation models for performance, reliability, and scalability.

Build AI pipelines for data ingestion, preprocessing, model inference, and post-processing.

Implement retrieval-augmented generation (RAG) solutions and integrate vector databases where applicable.

Develop prompt engineering strategies and optimize model outputs for accuracy, relevance, and consistency.

Integrate AI solutions with enterprise platforms, databases, cloud services, and existing business applications.

Design and implement monitoring, logging, testing, and evaluation frameworks for AI systems.

Optimize system performance, latency, throughput, resource utilization, and operational costs.

Apply security, privacy, access control, and responsible AI practices throughout the development lifecycle.

Troubleshoot complex system issues, conduct root-cause analysis, and implement corrective actions.

Collaborate with data scientists, software engineers, product managers, architects, and business stakeholders.

Participate in architecture reviews, code reviews, technical documentation, and engineering best-practice initiatives.

Support continuous integration and continuous deployment (CI/CD) processes and production releases.

Stay current with emerging AI technologies, frameworks, and engineering practices.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.

Significant professional experience in software engineering, AI engineering, machine learning engineering, or distributed systems.

Strong programming skills in Python and/or another modern programming language.

Hands-on experience designing and developing production-grade AI or machine learning applications.

Understanding of machine learning concepts, model inference, data processing, and model evaluation.

Experience developing RESTful APIs, microservices, and integrations with enterprise applications.

Knowledge of software architecture, distributed systems, data structures, algorithms, and system design.

Experience working with relational and/or NoSQL databases.

Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

Experience with Git, automated testing, CI/CD pipelines, and modern software development practices.

Strong analytical, debugging, communication, and problem-solving skills.

Ability to work independently in a remote environment and collaborate effectively with distributed teams.

Preferred Qualifications

Experience with generative AI applications and large language models (LLMs).

Knowledge of frameworks such as LangChain, LlamaIndex, Semantic Kernel, or comparable tools.

Experience implementing retrieval-augmented generation (RAG), embeddings, and vector search.

Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or FAISS.

Experience with AI agents, tool calling, workflow orchestration, and multi-step reasoning.

Knowledge of Docker, Kubernetes, and containerized application deployment.

Experience with model serving, inference optimization, and AI application observability.

Familiarity with MLOps practices, model versioning, and automated model evaluation.

Understanding of data governance, privacy, and security requirements for enterprise AI applications.

Experience working in large enterprise or consumer-facing technology environments.

Technical Skills

Programming: Python, SQL, and/or other relevant programming languages.

AI/ML: Machine learning, generative AI, LLMs, model inference, and model evaluation.

AI Frameworks: LangChain, LlamaIndex, or equivalent frameworks, where applicable.

Data & Retrieval: Data pipelines, embeddings, vector search, and RAG architectures.

Backend Engineering: REST APIs, microservices, and enterprise system integration.

Cloud & Infrastructure: AWS, Azure, or GCP; Docker and Kubernetes.

DevOps: Git, CI/CD, automated testing, deployment, monitoring, and logging.

Databases: SQL and NoSQL technologies

Key Skills

Education

Bachelor's or Master's degrees