Intern - LLM Software Engineer

About The Company Opkey is the leading Cloud Application Lifecycle Management (CALM) platform for Oracle, Workday, Salesforce, Coupa, and more. It cuts the costs and risks that drag down implementations and ongoing change, helping you go live on time, get more from your cloud app investments, and reach AI readiness faster. Opkey’s 20+ AI agents manage all five phases of the cloud application lifecycle—Define, Design, Configure, Test and Train.

Whether it’s a new implementation, a platform update, or business‑as‑usual change, Opkey handles it all: updates validated in hours, self‑healing tests, end‑to‑end integrations assured, configurations synced, and training updated in real time—all delivered in a single unified platform instead of a patchwork of disconnected tools.

Powered by Argus, a domain‑specific AI model trained on decades of expertise and terabytes of enterprise application data, Opkey automates configuration, testing, change impact analysis, and training across these applications—cutting manual effort by 80%, enabling 30% faster go‑lives, and slashing downtime risk by 92%

About The Role We are seeking highly motivated AI Interns who are passionate about Artificial Intelligence, Machine Learning, NLP, and Large Language Models (LLMs). This internship offers an opportunity to work on cutting-edge AI initiatives, contribute to research-driven projects, and collaborate with experienced engineers and researchers.

Internship Duration: 3–6 Months (with potential conversion to a Full-Time role based on performance)

Key Responsibilities

• Assist in the design, development, and evaluation of AI/ML models.
• Work on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI use cases.
• Conduct research, experimentation, and benchmarking of AI models and frameworks.
• Develop and optimize machine learning pipelines and model architectures.
• Analyze datasets, perform data preprocessing, and derive meaningful insights.
• Implement algorithms and prototypes to solve real-world business problems.
• Read, understand, and apply concepts from research papers to practical solutions.
• Collaborate with engineering and product teams to build AI-powered applications.
• Document findings, experiments, and technical learnings.

Preferred Candidate Profile

• Prior internship or project experience with AI startups, research labs, or academic AI research environments.
• Hands-on exposure to model development, Machine Learning, Deep Learning, NLP, LLMs, or Generative AI.
• Experience working on research-based projects or contributing to published research papers is highly desirable.
• Strong programming and problem-solving skills.
• Familiarity with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, or similar technologies.
• Prior internship experience in research labs will be an added advantage.
• Strong analytical thinking and eagerness to learn emerging AI technologies.

Required Skills

• Programming: Python
• Machine Learning & Deep Learning fundamentals
• NLP and LLM concepts
• Data Structures & Algorithms
• Research aptitude and experimentation mindset
• Excellent problem-solving and communication skills

Nice to Have (Strong Plus)

• Prior internship or research experience at leading AI labs or tech organizations — such as European institutions (INRIA, Max Planck Institute, ETH Zurich, EPFL, FAIR/Meta Paris, Oxford, Cambridge, or DeepMind London) or top US companies and AI startups (OpenAI, Anthropic, Google DeepMind, Meta AI Research, Microsoft Research, Mistral AI, Cohere, Hugging Face, AI21 Labs, IBM Research or equivalent).
• Hands-on experience with Small Language Model (SLM) pre-training, fine-tuning, or instruction tuning, including familiarity with SoTA techniques such as LoRA, QLoRA, DPO, RLHF, or Supervised Fine-Tuning (SFT).
• Experience building training data pipelines — including data collection, cleaning, deduplication, formatting, and tokenization for LLM/SLM training.
• Familiarity with distributed training frameworks such as DeepSpeed, FSDP, or Megatron-LM, and experience running training jobs on GPU clusters.
• Exposure to domain-specific data curation, annotation workflows, or synthetic data generation for model training.
• Experience with model evaluation, benchmarking (e.g., MMLU, HellaSwag, BIG-Bench), and ablation studies for language models.

Educational Qualifications

• Pursuing or recentl…