New Age Digital Marketing Manager

Position: New Age Digital Marketing Manager
Experience: 5-8 Years
Location: Onsite
Employment Type: Full-time

We’re on the hunt for a sharp, creative, and deeply curious marketing professional who thinks
beyond traditional playbooks. If you live on LinkedIn, obsess over AI tools, geek out on growth
experiments, and can craft compelling narratives for both enterprise decision-makers and indie
developers — this role was built for you.
You’ll own the full marketing charter — driving awareness and demand for our AI-powered products
while also generating qualified leads for our technology services catering to startups and enterprise
clients alike.

Key Responsibilities:

Core Digital Marketing
• Plan and execute high-ROI paid campaigns on Google, Meta, and LinkedIn — from keyword
strategy to ad copy to budget pacing.
• Own SEO end-to-end: technical audits, on-page optimisation, link building, and content
strategy to drive organic growth.
• Build and manage a content calendar across blogs, LinkedIn, and other relevant channels to
attract and nurture the right audience.
• Monitor, analyse, and report campaign performance using tools like Google Analytics, Search
Console, and platform dashboards.
• Collaborate with designers and developers to craft high-converting landing pages, email
sequences, and marketing funnels.
• Conduct competitive research and produce monthly insights reports to inform strategy and
identify growth opportunities.

New Age & Growth Marketing
• Leverage cutting-edge AI tools (ChatGPT, Perplexity, Jasper, etc.) to supercharge content
production, research, and campaign execution.
• Build a brand presence in tech-native communities — LinkedIn, X (Twitter), Discord, GitHub,
and developer forums — where our buyers actually hang out.
• Design and ship automated marketing workflows using no-code / low-code platforms (like
Zapier, Make, or HubSpot) to scale output without scaling headcount.
• Co-create sharp technical content — case studies, product explainers, use-case teardowns —
in close partnership with the engineering team.
• Conceptualise and execute virtual events, webinars, AMAs, and co-marketing initiatives
specifically targeted at the AI and startup ecosystem.
• Experiment relentlessly: A/B test messaging, creative, audiences, and channels; document
learnings and iterate fast.

Required Qualifications
• 3–5 years of hands-on digital marketing experience with a proven track record of generating
leads and building brand.
• Strong command over paid media platforms (Google Ads, Meta Ads Manager, LinkedIn
Campaign Manager).
• Working knowledge of SEO tools such as Ahrefs, SEMrush, or Moz.
• Genuine interest in AI and emerging technology — you follow the space, use the tools, and
speak the language.
• Experience marketing to technical audiences: developers, CTOs, founders, or product leaders.
• Proficient with no-code automation tools and basic analytics dashboards — you don’t wait for a
developer to set up a Zap.
• Strong written communication: you can write crisp ad copy as easily as a long-form thought
leadership post.
• Self-starter mindset — comfortable in a fast-moving startup environment where strategy and
execution often happen simultaneously.

What We Offer
• A front-row seat to building AI products from the ground up
• Direct exposure to marketing across India, the US, and global markets
• A team of engineers, designers, and builders who will actually collaborate with you
• Freedom to experiment — we measure results, not hours
• Competitive compensation
• Opportunity to grow fast in a company that rewards ownership

BONUS POINTS IF YOU HAVE
• Experience marketing SaaS products or AI tools directly to end consumers.
• A portfolio of technical content, viral posts, or community-building efforts in the developer /
startup ecosystem.
• Hands-on experience with HubSpot, Notion, or similar CRM/marketing stack tools.
• An active personal presence on LinkedIn or X with a genuine following in your niche.

EDUCATIONAL BACKGROUND
We value proven thinking and real-world impact over pedigree — but we do look for graduates and
post-graduates from reputed Indian institutions (Top state universities, or equivalent) who bring
intellectual rigour alongside their marketing hustle.
B.Tech, B.Sc, BCA, BBA, MBA in Marketing, Communications, or related fields — all welcome. What
matters more is what you’ve built.

You’re the kind of person who:
• Has a side project, newsletter, or social media presence you’re proud of
• Can explain what a RAG pipeline is and write a punchy headline about it
• Thinks about distribution as much as creation
• Gets excited about a campaign that cost ₹0 and reached 10,000 people

GOOD TO HAVE UNDERSTANDING ON TOOLS & PLATFORMS:
• Experience in a SaaS, AI, or tech services company
• Hands-on with no-code automation tools (Zapier, Make, Airtable)- if no, will help you teach.
• Familiarity with developer communities (GitHub, Discord, Product Hunt, Hacker News)
• Basic understanding of the software development lifecycle (you don’t need to code, but
knowing what an API or MVP is helps)
• Experience with Product Hunt launches, community-led growth, or creator-style content
Google Ads,Meta Ads,LinkedIn Ads,Google Analytics,SEMrush / Ahrefs,HubSpot / Mailchimp
Zapier / Make,ChatGPT / Claude,Notion,Canva / Figma basics,X / Discord Webflow / Framer

WHY THIS ROLE IS DIFFERENT
• You won’t be executing someone else’s playbook — you’ll write the one we follow.
• You’ll market real AI products to real users and services to engineers and founders who have
zero patience for fluff.
• You’ll have the latitude to experiment, the budget to test, and the team to back you up.
• You’ll work shoulder-to-shoulder with engineers, product designers, and founders — not sit in a
silo.
Think you’re built for this? We’d love to see your work — apply with your CV and a short note on your best campaign.

About the Role

Experience: 1-2 years

At Tecorb Technologies, we are building the future of intelligent systems by leveraging cutting-edge AI/ML and Generative AI technologies.

We are looking for a driven and technically grounded developer with 1-2 years of hands-on experience to join our AI/ML & Generative AI team. This role is suited for professionals who have moved beyond entry-level exposure and are ready to take ownership of AI/ML pipelines, contribute to real-world deployments, and grow into a senior AI engineer role. You will work on machine learning, deep learning, large language models (LLMs), and generative AI systems including GPT models, Transformers, diffusion models, and more.

If you connect dots, understand systems holistically, and are passionate about building intelligent applications — we want you.

Core AI/ML Knowledge Required

Machine Learning Fundamentals

• Supervised, unsupervised, and semi-supervised learning paradigms
• Classical ML algorithms: Linear/Logistic Regression, Decision Trees, Random Forests, SVMs, Gradient Boosting (XGBoost, LightGBM)
• Model evaluation: cross-validation, bias-variance tradeoff, precision/recall, ROC-AUC
• Feature engineering, data preprocessing pipelines, and handling imbalanced datasets
• Hands-on experience with scikit-learn, NumPy, Pandas

Deep Learning & Neural Networks

• Solid understanding of feedforward networks, backpropagation, and optimization (Adam, SGD, learning rate schedulers)
• Convolutional Neural Networks (CNNs) for image/vision tasks
• Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequential data
• Attention mechanisms and how they led to the Transformer architecture
• Experience with PyTorch and/or TensorFlow/Keras for model training and fine-tuning

Transformers & Large Language Models

Transformers are the backbone of modern AI. Candidates must have strong knowledge of:

• Transformer Architecture — Self-attention, multi-head attention, positional encoding, encoder-decoder structure
• BERT (Bidirectional Encoder Representations from Transformers) — masked language modeling, fine-tuning for classification, NER, QA
• GPT Models (GPT-2, GPT-3, GPT-3.5, GPT-4) — causal language modeling, prompt engineering, in-context learning, and API integration
• T5, FLAN-T5 — encoder-decoder models for text-to-text tasks like summarization, translation, and QA
• LLaMA, Mistral, Falcon — open-source LLM architectures, weight loading, and inference optimization
• Hugging Face Transformers library — model loading, tokenization, pipelines, and Trainer API
• Tokenization strategies — BPE, WordPiece, SentencePiece
• Positional encodings — absolute, relative (ALiBi, RoPE)

Generative AI Systems

• Generative Adversarial Networks (GANs) — basics of generator-discriminator training
• Variational Autoencoders (VAEs) — latent space, reconstruction loss, KL divergence
• Diffusion Models — forward/reverse diffusion, DDPM, Stable Diffusion, ControlNet
• Retrieval-Augmented Generation (RAG) — vector retrieval, chunking strategies, reranking
• Prompt engineering techniques — zero-shot, few-shot, chain-of-thought, ReAct
• Fine-tuning strategies — full fine-tuning, LoRA, QLoRA, instruction tuning, RLHF basics

NLP Fundamentals

• Text preprocessing — tokenization, lemmatization, stopword removal, vectorization (TF-IDF, embeddings)
• Core NLP tasks — sentiment analysis, Named Entity Recognition (NER), text classification, summarization, machine translation
• Word embeddings — Word2Vec, GloVe, FastText, and contextual embeddings from Transformers
• Semantic similarity, question answering, and dialogue systems

 

Key Responsibilities

• Design, build, fine-tune, and deploy ML models and generative AI pipelines with minimal supervision
• Integrate pre-trained models (GPT-4, LLaMA, Mistral, Stable Diffusion) into production applications via APIs or custom backends
• Implement end-to-end NLP systems — chatbots, content generators, summarizers, and RAG pipelines
• Perform data preprocessing, feature engineering, and dataset curation for training and evaluation
• Conduct model evaluation, benchmarking, error analysis, and iterative performance tuning
• Explore and implement reinforcement learning techniques (RLHF, reward modeling) to improve model interactions
• Build modular, scalable AI workflows using FastAPI, Docker, LangChain, and vector databases
• Stay current with AI research (arXiv, Hugging Face, OpenAI blogs) and prototype new ideas rapidly
• Write clear documentation for experiments, code, models, pipelines, and datasets

 

Required Skills & Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, AI/ML, or a related field
• 1-2 years of hands-on professional or project experience with ML/AI systems
• Strong Python proficiency with ML libraries: scikit-learn, TensorFlow, PyTorch
• Solid understanding of Transformer architecture and attention mechanisms
• Practical experience working with GPT models via API (OpenAI, Azure OpenAI, Groq) or open-source equivalents
• Working knowledge of Hugging Face ecosystem (Transformers, Datasets, PEFT)
• Familiarity with LLM fine-tuning techniques: LoRA, QLoRA, or full fine-tuning
• Understanding of vector databases and embedding workflows (FAISS, ChromaDB, Pinecone)
• Knowledge of APIs, JSON, and RESTful application design
• Proficiency with Git, Jupyter Notebooks, and Linux command-line tools

 

Nice to Have (Not Mandatory)

• Familiarity with MLOps tools: Weights & Biases, MLflow, DVC, or TensorBoard
• Basic knowledge of reinforcement learning

 

What You’ll Get

• Hands-on experience on real AI/ML projects and production-ready systems from day one
• Mentorship and career development from senior AI/ML engineers and researchers
• Access to state-of-the-art tools, compute infrastructure, and paid API credits
• Opportunity to contribute to high-impact solutions in AI automation, GenAI workflows, and enterprise applications
• A learning-driven, inclusive, and collaborative work environment
• Fast growth track with exposure to client-facing work, research, and system design

 

We are looking for an Engineer who understands systems and connects the dots — not just a developer.

Tecorb Technologies | Noida | tecorb.com