At Conclase Consulting, we provide top notch IT solutions and support services to help you transform your business into an Intelligent Enterprise, redefine the customer experience, deliver a step change in productivity, and inspire total workforce engagement to achieve game-changing outcomes.
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
Method of Application
Signup to view application details.
Signup Now
