Machine Learning Engineer at Conclase Consulting

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

• 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