Position Overview :

In this role, you will spearhead the design, development, and optimization of both traditional machine learning systems and LLM-based applications. Youll partner with enterprise stakeholders to translate complex business challenges into AI-driven solutions, ensuring models perform reliably in production and deliver tangible ROI.

You will also lead MLOps initiatives building automated pipelines for model training, deployment, monitoring, and evaluation and implement best practices for inference optimization, cost efficiency, and continuous quality assurance. This position offers the opportunity to shape AI engineering standards and advance cutting-edge capabilities at scale.

Key Responsibilities :

- Design and implement traditional ML and LLM-based systems to deliver scalable AI solutions

- Optimize model inference for performance and cost-efficiency across cloud platforms

- Fine-tune foundation models using LoRA, QLoRA, and adapter layers to meet client specifications

- Develop and apply prompt engineering strategiesincluding few-shot learning, chain-of-thought, and RAG frameworks

- Architect and build robust backend infrastructure (APIs, microservices) to support AI-driven applications

- Implement and manage end-to-end MLOps pipelines, automating model training, deployment, and monitoring

- Design continuous monitoring and evaluation systems to ensure model accuracy, performance, and compliance

- Create automated testing frameworks and CI/CD pipelines to guarantee model quality and reliability

Critical Success Factors :

- Successful deployment of AI-powered applications in production with low latency and high throughput

- Demonstrated cost savings through optimized inference and resource-efficient model tuning

- High availability and reliability of MLOps pipelines, reflected in reduced downtime and streamlined workflows

- Effective collaboration with cross-functional teams resulting in on-time, on-budget project deliveries

Education and Experience :

- Bachelors degree in Computer Science, Artificial Intelligence, Data Science, or a related field

- 4+ years of experience in AI/ML engineering, software development, or data-driven solution delivery

- Proven expertise in LLM fine-tuning (LoRA, QLoRA, adapter layers) and inference optimization

- Strong backend development experience using Python with FastAPI or Flask

- Hands-on experience with cloud ML services (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)

- Familiarity with MLOps frameworks, orchestration tools (Airflow), and CI/CD pipelines

Essential Skills and Competencies :

Technical Skills :

- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers)

- Deep understanding of LLM serving tools (vLLM, TensorRT-LLM, SGlang)

- Experience with RESTful API development and vector/traditional databases (PostgreSQL, Redis)

- Hands-on expertise in AWS, GCP, Azure, and container orchestration (Docker, Kubernetes)

- Familiarity with MLOps tools for monitoring, evaluation, and CI/CD automation

Soft Skills :

- Strong problem-solving and analytical thinking

- Excellent communication, able to convey complex AI concepts to non-technical stakeholders

- Adaptability to shifting priorities in an agile, project-based environment

Behavioural Strengths :

- Detail-oriented with a quality-first mindset

- Proactive and self-motivated with a passion for continuous learning

- Collaborative team player who fosters knowledge sharing and mentorship

Leadership Skills :

- Ability to drive end-to-end AI projects, coordinating across cross-functional teams

- Strategic vision to guide technology choices and best practices in AI/ML engineering

- Mentorship capability to support junior engineers and elevate team skillsets