Job Summary
We are looking for an experienced
AI & Generative AI Developer
who can work across the AI spectrum—from classical machine learning models to cutting-edge Generative AI applications. The role demands strong experience in building ML models using regression, classification, and tree-based algorithms, along with hands-on exposure to LLMs and generative frameworks like GPT, Stable Diffusion, and LangChain.
Key Responsibilities
🔹 Classical AI/ML
Design and implement
supervised and unsupervised ML models
including:
Linear Regression, Logistic Regression
Decision Trees, Random Forest, XGBoost
Naive Bayes, K-Means, SVM, PCA, etc.
Preprocess and analyse structured/tabular datasets
Evaluate models using metrics like accuracy, precision, recall, ROC-AUC, and RMSE
Build
predictive models
, deploy them into production, and monitor performance
Collaborate with business teams to translate requirements into ML use cases
🔹 Generative AI (GenAI)
Build and fine-tune
LLMs
(e.g., GPT, LLaMA, PaLM) for summarisation, Q&A, document generation, etc.
Implement
prompt engineering
,
RAG pipelines
, and
vector database integrations
Use libraries like
Hugging Face Transformers, LangChain, and LlamaIndex
Develop APIs to expose GenAI models in real-time apps
Optimise model inference using quantisation, batching, etc.
Ensure safe, explainable, and bias-free output in alignment with AI ethics guidelines
Required Skills & Qualifications
Bachelor’s or Master’s in Computer Science, Data Science, Statistics, or related field
Strong programming skills in
Python
, with experience in
NumPy, Pandas, Scikit-learn
Proficiency in
classical ML algorithms
(regression, trees, naive Bayes, etc.)
Experience with
LLM frameworks
like OpenAI API, Hugging Face, and LangChain
Understanding of
transformer architecture
, NLP, embeddings, and tokenisation
Familiarity with
REST API development
using FastAPI/Flask
Exposure to
cloud platforms
(AWS/GCP/Azure) and
Docker/Kubernetes
Preferred / Nice to Have
Experience with
deep learning
(TensorFlow, PyTorch)
Exposure to
image/audio/video generation
using models like DALL·E, Stable Diffusion, Whisper
Familiarity with
RAG
,
LLMOps
, and
vector stores
(FAISS, Pinecone, Weaviate)
Knowledge of
MLOps pipelines
,
model monitoring
, and
CI/CD for ML
