Machine Learning Engineer

Mid-Level Machine Learning Engineer

Job Role:

Mid-Level Machine Learning Engineer

Total Experience:

6+ years

Relevant Experience:
 
Min 1 year in Machine Learning / LLMs / Agentic Workflows

Job Location:

Kolkata / Indore (hybrid)

Education:

Bachelor's or Master's in Computer Science, Engineering, AI, Data Science, or equivalent 

Job Description

We are seeking a
hands-on, mid-level Machine Learning Engineer
to join our core AI platform team responsible for building
scalable, reusable AI services
and
agentic automation frameworks
that power products across multiple industries. The ideal candidate is strong in ML/LLM fundamentals, comfortable working in a fast-paced environment, and able to mentor junior engineers.

This role will contribute to the design, development, and deployment of
multi-modal ML pipelines
,
LLM-driven workflows
, and
agent-based systems
while ensuring the platform is generic, extensible, and robust.

AI/ML Roles and Responsibilities

•       Design, develop, and deploy
machine learning models
including NLP/LLM-based components, classification pipelines, embeddings, and generative models.

•       Build and optimize
LLM-powered features
such as summarization, reasoning, classification, extraction, recommendations, or conversational flows.

•       Develop
agentic workflows
using frameworks like LangChain, LangGraph, DSPy, or custom orchestration mechanisms.

•       Implement multi-step reasoning, tool-calling, memory structures, and task automation using agent-based architectures.

Reusable AI Services Engineering

•       Build
scalable, reusable ML microservices
that can be consumed by multiple product teams.

•       Design
generic APIs, schemas, prompts, and configuration-driven patterns
to support multi-vertical use cases.

•       Build shared components that abstract domain-specific logic behind configurable templates and rules.

•       Collaborate with architects to ensure services follow best practices for performance, reliability, and maintainability.

Data Pipelines & Model Lifecycle

•       Design and maintain
data processing pipelines
for training, evaluation, and inference.

•       Participate in model evaluation, fine-tuning, benchmarking, and experimentation.

•       Implement confidence scoring, model monitoring, drift detection, and quality assurance practices.

•       Collaborate with MLOps engineers to package, deploy, scale, and monitor ML models in production.

Colaboration & Mentorship

•       Work closely with backend, DevOps, product, and domain teams to integrate ML capabilities into the platform.

•       Mentor junior developers and help them grow in ML engineering best practices.

•       Participate actively in design reviews, code reviews, and platform-level architecture discussions.

•       Communicate technical ideas clearly and work collaboratively in a cross-functional environment.

Documentation & Process

• Document models, prompts, APIs, workflows, experiments, and platform components.

•       Follow best practices in version control, testing, evaluation, and observability for ML components.

•       Contribute to continuously improving engineering processes, coding standards, and platform guidelines.

Must Have (Core Skills)

•       Strong hands-on experience in
Python
, PyTorch/TensorFlow, and ML model development.

•       Experience with
LLMs
(OpenAI, Llama, Mistral, DeepSeek, etc.) for building intelligent features or workflows.

•       Practical exposure to
agentic frameworks
(LangChain, LangGraph, ReAct, DSPy, or similar).

•       Solid understanding of NLP techniques and experience working with embeddings, RAG, or prompt engineering.

•       Ability to build ML-driven microservices and APIs for consumption by other teams.

•       Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).

•       Strong analytical, debugging, and problem-solving skills.

•       Ability to guide and mentor junior team members on technical tasks.

Good to have:

•       Experience with
multi-modal models
(text, audio, image, video).

•       Hands-on experience with vector databases (Pinecone, Weaviate, Milvus, etc.).

•       Knowledge of distributed systems, event-driven architectures, or real-time inference pipelines.

•       Exposure to MLOps tools such as MLflow, Weights & Biases, KServe, Triton Server.

•       Basic knowledge of domain-driven design or building platform-level shared services.

•       Experience in designing evaluation frameworks or automated testing for LLMs/agents.