Machine Learning Engineer - Artificial Intelligence Solutions (4-6 yrs)

Role : ML Engineer

Role Overview :

We are seeking a skilled and innovative Machine Learning Engineer with expertise in Large Language Models (LLMs) to join our team. The ideal candidate has hands-on experience developing, fine-tuning, and deploying LLMs, alongside a deep understanding of the machine learning lifecycle. This role involves building scalable AI solutions, collaborating with cross-functional teams, and contributing to cutting-edge AI initiatives.

Key Responsibilities :

Model Development & Optimization :

- Develop, fine-tune, and deploy LLMs like OpenAI's GPT, Anthropic's Claude, Google's Gemini, or AWS Bedrock.

- Customize pre-trained models for specific use cases, ensuring high performance and scalability.

Machine Learning Pipeline Design :

- Build and maintain end-to-end ML pipelines, from data preprocessing to model deployment.

- Optimize training workflows for efficiency and accuracy.

Integration & Deployment :

- Work closely with software engineering teams to integrate ML solutions into production environments.

- Ensure APIs and solutions are scalable and robust.

Experimentation & Research :

- Experiment with new architectures, frameworks, and approaches to improve model performance.

- Stay updated with advancements in LLMs and generative AI technologies.

Collaboration :

- Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to align ML solutions with business goals.

- Provide mentorship to junior team members as needed.

Required Qualifications :

Experience :

- At least 5 years of professional experience in machine learning or AI development.

- Proven experience with LLMs and generative AI technologies.

Technical Skills :

- Proficiency in Python (required) and basic knowledge of Java is needed

- Hands-on experience with APIs and tools like OpenAI, Anthropic's Claude, Google Gemini, or AWS Bedrock.

- Familiarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face.

- Strong understanding of data structures, algorithms, and distributed systems.

Cloud Expertise :

- Experience with AWS, GCP, or Azure, including services relevant to ML workloads (e.g., AWS SageMaker, Bedrock).

Data Engineering :

- Proficiency in handling large-scale datasets and implementing data pipelines.

- Experience with ETL tools and platforms for efficient data preprocessing.

Problem Solving :

- Strong analytical and problem-solving skills, with the ability to debug and resolve issues quickly.

Preferred Qualifications :

- Experience with multi-modal models and generative AI for images, text, or other modalities.

- Understanding of ML Ops principles and tools (e.g., MLflow, Kubeflow).

- Familiarity with reinforcement learning and its applications in AI.

- Knowledge of distributed training techniques and tools like Horovod or Ray.

- Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related field.