Agentic
About the Role
We are seeking a talented and motivated Data Scientist with expertise in machine learning and cutting-edge Agentic AI technologies. This role is ideal for recent graduates or early-career professionals with 1 year to 3 years' experience who are passionate about building intelligent systems specially for business process automations and want to work at the intersection of traditional ML and modern LLM-based solutions.
Role Description
As an agentic AI-ML Engineer, you will design, develop, and deploy machine learning models and AI agents that solve real-world business problems. You will work with structured and unstructured data, build predictive models, and create autonomous AI systems using state-of-the-art frameworks. This is a hands-on role where you'll contribute to the full lifecycle of AI/ML projects from conception to production.
Required Skills
Technical Skills:
- Strong foundation in
Data Science
and Machine Learning
concepts (supervised/unsupervised learning, feature engineering, model evaluation) - Hands-on experience with
ML frameworks
such as scikit-learn, TensorFlow, PyTorch, or similar - Proficiency in
Python
and data manipulation libraries (Pandas, NumPy, Matplotlib/Seaborn) - Solid understanding of
LLM fundamentals
including prompt engineering, embeddings, vector databases, and retrieval techniques - Practical experience with
Agentic AI frameworks
such as LangChain
and LangGraph
- Knowledge of building AI agents with reasoning, planning, and tool-use capabilities
- Familiarity with
RAG (Retrieval-Augmented Generation)
architectures - Understanding of
APIs
and integration patterns for AI systems - Experience with
SQL
and working with databases
Preferred Skills:
- Exposure to cloud platforms (AWS, Azure, or GCP) for ML deployment
- Knowledge of MLOps practices and tools (MLflow, Docker, CI/CD)
- Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS)
- Familiarity with OpenAI API, Anthropic Claude, or other LLM providers
- Understanding of fine-tuning techniques for LLMs
- Experience with version control systems (Like Git and Bitbucket)
Soft Skills:
- Strong problem-solving and analytical thinking
- Excellent communication skills to explain technical concepts to non-technical stakeholders
- Ability to work collaboratively in cross-functional teams
- Self-motivated with a passion for staying updated on AI/ML advancements
- Attention to detail and commitment to delivering quality work
Roles and Responsibilities
Machine Learning & Data Science:
- Develop, train, and evaluate machine learning models for predictive analytics, classification, regression, and clustering tasks
- Perform exploratory data analysis (EDA) to extract insights and identify patterns in complex datasets
- Design and implement feature engineering pipelines to optimize model performance
- Conduct model validation, hyperparameter tuning, and performance monitoring
- Work with structured and unstructured data from various sources
Agentic AI & LLM Development:
- Build intelligent AI agents using LangChain and LangGraph frameworks that can reason, plan, and execute tasks autonomously
- Design and implement multi-step workflows with tool-calling capabilities and decision-making logic
- Develop RAG systems that combine LLMs with proprietary knowledge bases
- Create prompt templates and chains for optimal LLM performance
- Integrate LLMs with external APIs, databases, and third-party services
- Implement memory management and state handling for conversational AI systems
Deployment & Optimization:
- Deploy ML models and AI agents into production environments
- Optimize model inference for performance and cost-efficiency
- Monitor model performance and implement retraining pipelines as needed
- Document technical implementations, architectures, and best practices
Collaboration & Innovation:
- Collaborate with product managers, engineers, and business stakeholders to understand requirements and deliver AI solutions
- Stay current with the latest developments in AI/ML, especially in the LLM and agentic AI space
- Participate in code reviews and contribute to team knowledge sharing
- Propose innovative solutions to leverage AI for business value
Qualifications
Education
: Bachelor's or Master's degree in CS, IT, Data Science or AI Experience
: 1-3 years of hands-on experience in data science, machine learning, or AI development (We would like to only consider candidates with max 3 years exp) Freshers can also be considered provided you are
possessing
above mentioned skills and passionate to build your career as an AI
ML Engineer
What is in it for you:
- Live Use-Cases from Day 1: Drive production AI and automation projects across Banking, NBFC, Manufacturing and Insurance.
- High-Performing Culture: Work alongside passionate, subject-matter experts
- who thrive on innovation and excellence.
- Attractive Rewards: Competitive salary, performance-linked bonuses, and equity grants to recognize your impact.
- Modern, Central Office: AWFIS workspace in Thane Wagle Estate, with all amenities so you can focus on building solutions.
Interview Process and Expectations
- CV Screening: Applications are reviewed to ensure that the experience, skills, and keywords mentioned are relevant to the job profile.
- Telephonic Interview: Candidates must be able to clearly explain their knowledge and expertise related to the skills mentioned on their resume.
If a candidate is unable to justify or elaborate on the listed skills during the call, their application will be discontinued at this stage. - Technical Round (Face-to-Face): A mandatory in-person interview will be conducted at our Thane (West) office.
- Candidates must attend in formal attire.
- Please carry your laptop to discuss project work that demonstrates the application of relevant skills.
- Come well-prepared for technical discussions.
- HR Round: Candidates who clear the technical round with a satisfactory score or above will have their HR interview on the same day.
Those with an average score may either be placed on hold for further evaluation or not proceed to the next stage. - Final (Management) Round: Shortlisted candidates will meet the Leadership Team for a final discussion.
Attendance in formals and carrying your laptop is mandatory. - Post-Interview Communication: Within one week of the interview, the team will reach out:
- Selected candidates will receive a call.
- Others will be notified via email if not shortlisted further.
Role & responsibilities
Preferred candidate profile
