Company Overview :

Skillence Tech is a rapidly growing Technology consulting companies help businesses leverage digital solutions, AI, cloud, and IT strategies to drive growth, efficiency, and innovation. We are specializing in AI-powered solutions for the financial services industry.

We develop and deploy cutting-edge machine learning models to enhance fraud detection, risk management, and customer experience for our clients. Our team is composed of talented engineers and data scientists dedicated to pushing the boundaries of AI innovation.

Role & Responsibilities :

- Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.

- Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.

- Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.

- Should have knowledge of advanced prompting techniques.

- Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).

- Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.

- Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.

- Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.

- Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.

- Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.

- Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.

- Understand and identify areas of improvement across businesses and participate in solution identification and implementation.

- Should be able to work as an Individual Contributor on new and existing projects.

- Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate :

- Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

- Mandatory (Experience 1) : Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

- Mandatory (Experience 2) : Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

- Mandatory (Experience 3) : Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

- Mandatory (Experience 4) : Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

- Mandatory (Experience 5) : Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

- Mandatory (Experience 6) : Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

- Mandatory (Experience 7) : Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

- Mandatory (Experience 8) : Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

- Mandatory (CTC) : The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

- Preferred (Experience 1) : Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

- Preferred (Experience 2) : Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

- Mandatory ( Age ) : Candidate Should be Below 28 Years.

- Preferred (Experience 3) : Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

- Preferred (Experience 4) : Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

- Preferred (Company) : Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.